Hybrid Reasoning Approach in Clinical Decision Support Systems

By

SYED SAOOD ZIA Department of Computer Science

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy (Information Technology)

at

Graduate School of Engineering Sciences and Information Technology Faculty of Engineering Sciences and Technology Hamdard University Karachi, Pakistan

July, 2016 Copyright c Syed Saood Zia, 2016 All right reserved. Printed by: Hamdard University Graduate School of Engineering Sciences and Information Technology Faculty of Engineering Sciences and Technology Hamdard University

Doctoral Defense

We hereby recommend that the student SYED SAOOD ZIA Roll No.: ITP - F06 - 104 Enrollment No.: ICK - IT - 06 - 0023 may be accepted for Doctor of Philosophy Degree.

Doctoral Defense Committee

Held on 26 − 07 − 2017 DD - MM - YYYY

Supervisor: P rof. Dr. P ervez Akhtar Signature with Date

Co-Supervisor: (if appointed) Signature with Date

GEC Member 1: P rof. Dr. Aqeel−ur−Rehman Internal Signature with Date

GEC Member 3: Assoc. P rof. Dr. T ariq Javid Ali Internal Signature with Date

GEC Member 3: P rof. Dr. Shahid Hafeez Mirza, SSUET, P akistan External GEC Member, Univesity and Country

External Evaluator 2: Dr. Nadeem Mahmood, University of Karachi, P akistan Local External Expert Name, Univesity and Country

External Evaluator 3: P rof. Dr. Coskun BAY RAK, University of Arkansas, USA Foreign Expert Name, Univesity and Country

External Evaluator 4: P rof. Dr. Xiaohong Gao, Middlesex University, UK Foreign Expert Name, Univesity and Country

COUNTERSIGNED

Dated: DD - MM - YYYY Dean FEST Graduate School of Engineering Sciences and Information Technology Faculty of Engineering Sciences and Technology Hamdard University

Certificate of Approval

It is certified that Syed Saood Zia s/o Syed Zia Uddin Ahmed bearing enrollment no. ICK – IT – 06 - 0023 has successfully completed his PhD

(Information Technology) research study entitled Hybrid Reasoning Ap- proach in Clinical Decision Support Systems under my supervision and his PhD dissertation meets the highest scholarly as set by the Hamdard

University, Karachi, Pakistan.

Dated: Prof. Dr. Pervez Akhtar DD - MM - YYYY PhD Supervisor Graduate School of Engineering Sciences and Information Technology Faculty of Engineering Sciences and Technology Hamdard University

PhD Thesis Report Approval

Dated: DD - MM - YYYY

I hereby recommend that the project prepared and successfully defended under my su- pervision entitled: Hybrid Reasoning Approach in Clinical Decision Support Systems By

SYED SAOOD ZIA be accepted in partial fulfillment of the requirement for the degree of Doctor of Philosophy in Information Technology from Graduate School of Engineering Sciences and Information Technology, Hamdard University.

Prof. Dr. Pervez Akhtar Deputy Director GSESIT PhD Supervisor

Chairman Postgraduate Dean FEST Graduate School of Engineering Sciences and Information Technology Faculty of Engineering Sciences and Technology Hamdard University

PhD Dissertation Submission Declaration

I certify the following about the research study entitled Hybrid Reasoning Approach in Clinical Decision Support Systems submitted for the degree of Doctor of Philosophy.

I hereby declare that: a. This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text and bibliography. b. My dissertation (or any significant part of my dissertation) is not substantially the same as any that I have submitted, or that is being concurrently submitted, for a degree or diploma or other qualification at the Hamdard University or any other University or similar institutions. c. The work(s) are not in any way a violation or infringement of any copyright, trade- mark, patent, or other rights whatsoever of any person. d. All research integrity requirements have been complied with. e. No conflict of interest with the supervisor for this research work.

I certify that the information provided in the form is correct.

Dated: Syed Saood Zia DD - MM - YYYY PhD Scholar I would like to dedicate this research work to my Mother, Rehana Begum (Late)

i Acknowledgements

I am extremely appreciative to all those person who, directly or ramblingly, are responsi- ble for this thesis coming into being. I want to express my heartfelt thanks to Professor Dr. Pervez Akhtar who has been far more than an advisor. With his patience, guidance, inspiration, and overwhelming support, I was able to conquer my own Everest. It has always been fun and a great experience to work with him. I am also thankful to my GEC members Professor Dr. Shahid Hafeez Mirza, Professor Dr. Aqeel-ur-Rehman and Dr. Tariq Javid Ali for all their support, encouragement, intuitive ideas and for sharing interesting research thoughts during my studies.

I am grateful to Dr.Asghar who is working as an oncologist consultant in Memon Medical Hospital Karachi-Pakistan, and Dr. Rufina Soomro working as an oncologist consultant in Liaquat National Hospital Karachi-Pakistan, for always being cooperative in providing me the knowledge that is prerequisite in this research.

The work presented in this thesis would not have been possible without the help of my supervisor, GEC Committee members and medical consultants. Without their priceless guidance and support, the presented thesis has not been possible.

I would like to thank both foreign examiners Prof. Dr. Coskun Bayrak, Department of Computer Science, University of Arkansas USA and Prof. Dr. Xiaohong Gao, Depart- ment of Computer Science, Middlesex University UK, and national examiner Dr. Nadeem Mahmood, Associate Professor, Department of Computer Science, University of Karachi, who evaluated my PhD thesis and advised to make some positive modification that is helpful to develop my PhD thesis as per international standard.

I am very grateful to Prof. Dr. Vali Uddin, Dean, Faculty of Engineering Sciences and Technology (FEST), Hamdard University who have guided me to revise my thesis accord- ing to the required standard. A special thanks to Dr. Arshad Aziz, without his invaluable support, guidance, and encouragement, I could not have been able to publish my research

ii papers in ISI indexed Journals. I also admire the continuous support, encouragement and guidance from Muhammad Iqbal Khan, Deputy Director GSESIT. I also appreciate the support and encouragement from Dr. Shakil Ahmed, Chairman, Computer Engineering Department at Sir Syed University of Engineering and Technology, who really supported me to complete this thesis in a given constraint of time.

I am very much grateful to my mentor Syed Muhammad Mujahid Sultani for his encour- agement and motivation to start and being through with this thesis work successfully. I appreciate the efforts of my friend Mr. Farrukh Khan, senior software engineer at Abacu- soft, who has supported me while developing proposed system. Many thanks forwarded to my colleague Mr. Idris Mala and the fellow PhD students for all the great time together. Thanks to Mrs. Shugfta Yasmeen for her help in proofreading.

Thanks to all the anonymous reviewers of my papers for their valuable feedback about the research. Many appreciations are also proceeded to the staff at GSESIT, Hamdard Uni- versity for always being helpful. In particular, thanks to Mr. Safwan Ahmed, Mr. Mazhar Hussain Shangi, Mr. Moin Uddin, Ms. Roomesa and more. Further, I appreciate the ser- vices received from Mr. Farooq and Mr. Fayaz during the course work and Mr. Faisal and Mr. Adil during the research work phases, respectively. Thanks to all for timely supply of tea, cookies, and chicken sandwiches.

I am very much thankful to my all friends, relatives, siblings; especially thanks to my friends Mr. Ejaz Lateef, Mr. Adnan Zahoor, Mr. Jawaid Shabir, Mr. Sarfaraz Natha, Mr. Muhammad Naseem and Mr. Tauseef Mobeen who have always inspired me through- out these years.

I would like to remember my father Syed Zia Uddin Ahmed, for their everlasting en- couragement and support during research work. Importantly, I must acknowledge love and warmth of my mother Rehana Begum (Late) for all the success of my efforts. For last, and most important, I am grateful beyond words to my wife Hiba Mujahid, my son Syed Muhammad Bin Saood and my daughters Maira Saood & Iffat Saood, they have encouraged and supported me always. I would not have been able to go through the process and finish the work without their invaluable love and support.

Syed Saood Zia

iii Abstract

Through the advent of technological progression, different computer aided applications were introduced during last four decades to supplement the diagnosis and treatment phases of patient care. Now at different levels initiatives have been taken to encourage the medical practitioners for implementing these high-tech computer applications in their everyday clinical practices to enhance the graph of human well-being. Clinical decision support systems (CDSSs) were introduced as an ideal computer based application to in- fluence the medical diagnosis process with its capability to store large extent of data and provide prerequisite data at the time of patient evaluation phases without wasting time. However the efficient progression of CDSS impeded by a number of obstacles which if addressed could potentially unlock the significance of these systems.

This research work reveals the comprehensive detail of the different CDSSs that were proposed during the last four decades after the innovation of these computer systems and then draw attention towards the desirable features of CDSSs that were found as the research gaps during literature review. This study is conducted with an aim to provide a CDSS which is proficient enough to overcome the grand challenges that were arose during the effective deployment of these systems.

This research work presents an online Knowledge Based Clinical Decision Support System (KBCDSS) that is deployed as an effective prototype application in medical domain to significantly aid medical experts in their routine clinical practices. KBCDSS is a multiple disease diagnosis system with the proficiency to gather medical experts over a single plat- form through web. This system follows the pattern of Knowledge Data Discovery (KDD) process to extract the knowledge that is prerequisite in the patient evaluation stages. In order to accomplish an effective functioning of this system certain course of action is followed for data analysis on the Wisconsin breast cancer data set from the UCI Machine Learning Repository and implements that medical data set on the proposed system.

iv The proposed KBCDSS initially pursues the pre-processing steps of KDD process to perform the knowledge acquisition task and proposed knowledge acquisition algorithm which efficiently update prerequisite medical data into data warehouse. The data ware- house server retains different medical records that are stored in relational tables. Using the technique of DBMS we have proposed an algorithm for the construction of Knowledge Base (KB) and its representation.

To perform mining task, we have proposed hybrid Case Base Reasoning(CBR)cycle where CBR and Support Vector Machine (SVM) are used as an inference mechanism to carryout more accurate conclusion and results. CBR is implemented as a core methodology in our model and we have proposed case retrieval algorithm for case retrieval phase of the CBR technique that are retrieving similar cases from KB. After that reinstantiation strategy is implemented in case reuse phase of the CBR technique for case adaption, that is simply copy the diagnosis of most similar case being the suggested solution of new input case. In case, alike cases are not present in KB, then we employ SVM for predicting the solution for new case. SVM is used for classification of data as well as predict the solution of new input case. After that, the concept of Group Clinical Decision Making (GCDM) is implemented in case revise phase where number of experts of same medical domain gives their opinion for the solution of new input case. For positive opinion from medical ex- perts, new case is now kept into KB which is the part of relational DB for future guidance.

The proposed KBCDSS is competent enough to provide comprehensive structural knowl- edge to its users within the very short span of time which is extremely supportive during the process of diagnosis and treatment of diseases. The efforts to develop this application were aimed to fulfill the research gaps and strengthen the weakness of previously existing CDSSs so that the deployment of these computer based systems become general and every medical personals can also easily use these systems by their own without the supervision of computer experts during the patient-care phases.

v List of Keywords

Artificial Intelligence, Case Based Reasoning, Clinical Decision Support Systems, Data Mining, Database, Database Management Systems, Decision Support Systems, Informa- tion Technology, Knowledge Acquisition, Knowledge Base, Knowledge based Systems, Knowledge based Clinical Decision Support Systems, Knowledge Data Discovery, Knowl- edge Management, Knowledge Representation, Support Vector Machine.

vi List of Publication

1 - Zia, SS, P. Akhtar, and TJA Mughal. "Schematic Cycle of Case-Based Reasoning Technique Implements in Clinical Decision Support Systems Used for Diagnosis of Liver Disease." Sindh University Research Journal-SURJ (Science Series) 47.2 (2015): 215-220. ISI indexed

2 - Zia, SS, P. Akhtar, and TJA Mughal. "Case Retrieval Process of CBR Technique Implements on Knowledge-Based Clinical Decision Support Systems (KBCDSS) for Diagnosis of Breast Cancer Disease." Sindh University Research Journal-SURJ (Sci- ence Series) 47.2 (2015): 241-246. ISI indexed

3 - Zia SS, P. Akhtar, R. Hussain, I. Mala. CBR: Cycle, Framework and Applications. World Applied Sciences Journal. 2014;32(7):1349-1355.

4 - Zia SS, P. Akhtar, TJA Mughal, I. Mala. Case Retrieval Phase of Case-Based Rea- soning Technique for Medical Diagnosis. World Applied Sciences Journal. 2014;32(3): 451-458.

5 - Mala I, P. Akhtar, TJA Mughal, SS Zia. Fuzzy Rule Based Classification for Heart Dataset using Fuzzy Decision Tree Algorithm based on Fuzzy RDBMS. World Ap- plied Sciences Journal. 2013;28(9):1331-1335.

6 - Mala I, P. Akhtar, SS Zia. Rational Database Models. Technocrat-Journal of Sci- ences & Technology, Pakistan Navy Engineering College, National University of Sci- ences & Technology, Karachi, Pakistan. ISSN: 1728 – 5690, pp: 41-44, 2012.

vii 7 - Zia SS, P. Akhtar, I. Mala, AR Memon. Clinical Decision Support System: A Hybrid Approach.IEEEP Karachi Center, 27th Annual Multi-topic International Symposium 2012, Karachi, Pakistan.

8 - Mala I, P. Akhtar, SS Zia, SH Mirza. Application of Fuzzy Relational Databases in medical informatics. InProceedings of the 14th IEEE Multi-topic conference (INMIC) 2011 Dec 22 (pp. 41-44).

viii List of Figures

2.1 The General Model of CDSS ...... 11 2.2 The Basic Structure of Knowledge-based System ...... 16 2.3 Support Vector as Separating Hyperplane between two Classes ...... 18 2.4 NeuroMate by Renishaw ...... 29 2.5 Pathfinder by Prosurgics ...... 29 2.6 Renaissance by Mazor Robotics ...... 30 2.7 Robodoc by Curexo Technology Corp...... 31 2.8 RIO by MAKO Surgical Corp...... 32 2.9 iBlock by Praxim Inc...... 32 2.10 Navio PFS by Blue Belt Tech...... 33 2.11 Stanmore Sculptor by Stanmore Implants ...... 33 2.12 Da Vinci by Intuitive Surgical Inc...... 34 2.13 FreeHand by Freehand 2010 Ltd...... 35 2.14 Telelap ALF-X by SOFAR S.p.A...... 35

3.1 Structure of the case ...... 42 3.2 Case Library ...... 43 3.3 Schematic Cycle of CBR ...... 43

4.1 The Knowledge Data Discovery Processes ...... 52 4.2 A Proposed Framework of Knowledge-based Clinical Decision Support System (KBCDSS) ...... 54

5.1 Schematic Cycle of CBR Technique ...... 63 5.2 Case Retrieval Phase of CBR Cycle ...... 65 5.3 Case Reuse Phase in CBR Cycle ...... 68 5.4 A Linear Separable SVM ...... 70

ix 5.5 The Training Data are Linearly Separable ...... 72 5.6 Two Possible Separating Hyperplanes and their Associated Margins . . . . 73 5.7 Support Vectors are Shown with a Thicker Border ...... 74 5.8 A New Hybrid CBR Cycle ...... 76 5.9 The System Flow Diagram of Hybrid CBR Diagnosis Process ...... 77

6.1 Knowledge Acquisition Process in KBCDSS ...... 81 6.2 Cleaning the Garbage Data ...... 81 6.3 Construction of Knowledge Base ...... 82 6.4 Case Representation of a Breast Cancer Disease ...... 83 6.5 Diagnosis Screen of the Proposed System – KBCDSS ...... 83 6.6 Retrieve the Most Similar Cases from the Case Repository (Textual Mode) 84 6.7 Retrieve the Most Similar Cases from the Case Repository (Graphical Mode) 85 6.8 Adopt the Solution of the Most Similar Case that Retrieved from the Case Repository ...... 85 6.9 New Case with Suggested Solution ...... 86 6.10 Comments from Different Medical Experts ...... 87 6.11 Verified and Validate the Accuracy of the New Case Solution ...... 87 6.12 New Case is Stored in the Case Repository or Knowledge Base ...... 88

x List of Tables

2.1 Specific Disease Oriented Clinical Decision Support Systems (CDSSs) . . 21 2.2 Computer Assistant Robotic Systems for a Specific Medical Domain . . . 27

3.1 Evaluation Measures ...... 48 3.2 Confusion Matrix ...... 49

5.1 Predictive and Responsive Values of the Breast Cancer Data Set . . . . . 71

6.1 Wisconsin Breast Cancer Data Set Attributes ...... 80 6.2 Accuracy Achieved by the Proposed Similarity Algorithm and The Other Systems ...... 89 6.3 Measures for Evaluating SVM Classifier for True Class Value ...... 90 6.4 Confusion Matrix – True Class ...... 91 6.5 Measures for Evaluating SVM Classifier for Predicted Class Value . . . . 91 6.6 Confusion Matrix – Predicted Class ...... 92 6.7 Measures for Evaluating SVM Classifier for Overall Accuracy ...... 92 6.8 Confusion Matrix – Overall Accuracy ...... 92

xi List of Abbreviations

Abbreviation Abbreviation Detail AI Artificial Intelligence ARM Association Rule Mining CBR Case Based Reasoning CDSS Clinical Decision Support Systems DB Database DBMS Database Management Systems DDSS Diagnostics Decision Support Systems DM Data Mining DSS Decision Support Systems DT Decision Tree FL Fuzzy Logic GA Genetic Algorithms IC Intelligent Computing IS Information Systems IT Information Technology KA Knowledge Acquisition KB Knowledge base KBCDSS Knowledge-based Clinical Decision Support Systems KBS Knowledge-based Systems KDD Knowledge Data Discovery KM Knowledge Management KR Knowledge Representation ME Medical Experts MF Medical Field MI Medical Informatics MP Medical Practitioners NN Neural Network R&D Research and Development RQ Research Queries SVM Support Vector Machine

xii Table of Content

Dedication i

Acknowledgements ii

Abstract iv

List of Keywords vi

List of Publication vii

List of Figures x

List of Tables xi

List of Abbreviations xii

1 Introduction 1 1.1 Motivation ...... 2 1.2 Challenges ...... 3 1.3 Aim and Objectives ...... 4 1.4 Research Queries ...... 5 1.5 Research Contribution ...... 5 1.6 Outline of the Thesis ...... 7 1.7 Chapter Summary ...... 8

2 Literature Review 10 2.1 Background ...... 10 2.2 General Model of CDSSs ...... 11 2.3 Previous Work on CDSSs ...... 12 2.4 Types of CDSSs ...... 15 2.5 Recent Research Projects on CDSSs for a Specific Disease ...... 20

xiii 2.6 Surgical Robotic Systems ...... 25 2.7 Surgical Robotic Systems for a Specific Domain ...... 26 2.8 Research Gap ...... 36 2.9 Chapter Summary ...... 37

3 Method and Approaches 39 3.1 Introduction ...... 39 3.2 Knowledge Base and its Representation ...... 40 3.3 Case Based Reasoning ...... 41 3.4 Group Clinical Decision Making - GCDM ...... 46 3.5 Metrics for Evaluating Classifier Performance ...... 48 3.6 Chapter Summary ...... 49

4 Development of the Proposed Tool - KBCDSS 50 4.1 Introduction of the Proposed System - KBCDSS ...... 50 4.2 KBCDSS Architecture ...... 53 4.3 Chapter Summary ...... 61

5 Proposed Reasoning Techniques 62 5.1 Introduction to Data Mining Techniques ...... 62 5.2 Case Based Reasoning Technique ...... 63 5.3 Support Vector Machine (SVM) ...... 70 5.4 Proposed Hybrid CBR Cycle ...... 76 5.5 Chapter Summary ...... 78

6 Implementation of the Proposed System - KBCDSS 79 6.1 Breast Cancer Medical Data Set ...... 79 6.2 Experimental Analysis and Results ...... 81 6.3 Measures for Evaluating Classifier Performance ...... 88 6.4 Chapter Summary ...... 93

7 Discussion, Conclusion and Future Directions 94 7.1 Research Discussion ...... 94 7.2 Conclusions ...... 98 7.3 Future Research Directions ...... 98

xiv Appendices 100 Appendix A – Overview of the Appended Papers ...... 100 Appendix B – Software Tools ...... 104 Appendix C – Research Groups ...... 106 Appendix D – Resume ...... 108 Appendix E – Health-Care Standards ...... 110

References 126

xv Chapter 1

Introduction

This chapter introduces the PhD research work, motivation of the research, identify chal- lenges while developing clinical decision support systems (CDSS), research aim and objec- tives, research queries, research contributions and an outline of the thesis.

Healthcare concerns rise with awareness in people and now they are looking for improved medical facilities. Technological research and development is therefore accomplished in the field of medical science to increase the quality of healthcare. Medical practitioners (MP) are now employing different computer aided medical applications to increase the reliability of their diagnoses and treatments of the diseases. Medical domain knowledge plays an important role in carrying out functioning of these systems. This medical do- main knowledge is the particular signs and symptoms of the patients related to a specific disease along with its diagnosis and suggested treatment.

Medical practitioners while assessing the patients and creating diagnostic inferences on the basis of symptoms are necessitate making use of related similar cases and their diagno- sis. Reference information could help the medical practitioners to construct and analyze hypothetical sequence for the attainment of proper diagnosis [Shortliffe, 2012]. This avail- able reference information and required course of action collectively make the diagnosis more accurate and reliable. In virtue of this, medical domain knowledge is an asset for the medical practitioners. They need to acquire appropriate knowledge under the constraint of time [Ericsson, 2004]. Therefore several techniques have been scrutinized to get better

1 and prompt knowledge which could be more time efficient.

The technique of data mining (DM) is used for extracting viable information from the data available in raw form has gained much attention. By employing the technique of DM, knowledge is discovered from large databases (DB) [Prakash, 2013]. DM algorithms are deemed to be vital in the field of research and development. DM techniques bring together the data analysis methods with different algorithms to process large and complex sets of data [Kantardzic, 2011]. The major mining tasks are classification, clustering, sum- marization, link analysis, regression models and sequence analysis. For solving the DM tasks, different algorithms and techniques are employed namely: Support vector machine (SVM), neural network (NN), fuzzy logic (FL), rough set theory (RST), association rule mining (ARM), decision tree (DT), genetic algorithms (GA) and case based reasoning (CBR)[Salem, 2011].

Clinical decision support systems (CDSSs) is one of the computer aided medical ap- plication used to verify the reliability of medical expert’s decision making during the diagnoses and treatment phase [Bai et al., 2014]. Medical consultants could easily access to accurate, complete and on time information of the patient by means of CDSSs. This would not only reduce the errors but also increase the quality of decision making process in diagnosis and treating the diseases. CDSSs has been coined as an “active knowledge systems, which use two or more items of patient data to generate case-specific advice” [Xiao et al., 2013]. This mean CDSS is actually a decision support system (DSS) which employs the knowledge management (KM) in a manner that could easy provide assistance in decisions.

1.1 Motivation

Accuracy of decisions in diagnosis and treatment suggestion closely relates to the medical practitioners clinical experiences, learning and practices [Musen et al., 2014]. On com- paring the medical field (MF) with last century, medical practitioners are now facilitated with vast growth of technology that would work as a helping hand for them in decision making process. Since last decade there is an increasing ratio of technological progression in medical field and as a result different medical applications have been developed that

2 facilitates the medical practitioner’s decision making processes.

CDSS for the medical diagnosis and remedial measures is an ideal application of the information technology (IT) [Bai et al., 2014]. In the beginning, CDSSs were employed to make decisions for the medical experts (ME) during diagnosis and treatment. Medical experts would simply act according to the recommendations of the CDSS without making their own inferences [Xiao et al., 2013].

Recent studies shows that CDSSs are the dynamic knowledgeable information systems (IS) invented with an aim to advice the medical experts during diagnosis by taking into consideration different attributes of the patient [Fong et al., 2013]. In a nutshell CDSSs gives an advice to the medical consultants which based on some feature values of the pa- tient records. This research is carried out with an aim to provide clinicians an advanced, emerging and well structured clinical decision support system that could efficiently over- come the challenges faced by the existing CDSSs which as a result make their use less popular.

1.2 Challenges

The major challenges faced while developing clinical decision support systems (CDSSs) which slow down its efficient progression includes [Sittig et al., 2008]:

• Construction of web based model

• Multiple disease oriented framework

• Knowledge acquisition process

• Construction of knowledge base and knowledge representation scheme

• Inference mechanism and

• Human computer interaction using GUI.

3 1.3 Aim and Objectives

The advent of CDSS intended to support medical consultants during the phase of diagno- sis and treatment. After the long period of four decades next to the innovations of CDSSs their effective progression is still a critical task and it is difficult enough to execute the running of these systems during clinical practices.

In the era of 1970 to 1980, there was a significant research conducted to tackle the sci- entific issues that were arose in the implementation of CDSS. Technological progresses in the process of knowledge acquisition, representation of knowledge, and mechanized approach of reasoning directed towards a significant considerations which relates to en- coding, modeling and diffusion of human skills in a computerized dispensation form. Yet, there is an imperative requirement of a well structured, advance and emerging clinical decision support system that could help clinicians for diagnosis and treatment suggestion of diseases and minimize the challenges faced while developing a CDSSs.

The aim of our study is to make available an easily accessible, accurate and comprehensive knowledge about diseases that help clinicians in patient evaluation phase. This research work presents an online knowledge-based clinical decision support systems (KBCDSS) which has a well defined architecture that makes it competent enough to overcome the challenges that are mentioned in section 1.2. In order to resolve knowledge acquisition (KA) task, the proposed system employed knowledge data discovery (KDD) process for mechanized and useful mining of knowledge. Database management system (DBMS) is used for the construction of knowledge base (KB) and as a knowledge representation (KR) scheme.

Since DBMS is less capable to argue and conclude results we therefore used hybrid reason- ing approach that is schematic cycle of case based reasoning (CBR) and support vector machine (SVM) as inference mechanism in our model. Using this system medical prac- titioners of different medical domains gather over one platform through web where they can check and verify the reliability of their decision making.

4 1.4 Research Queries

In this research work, we have formulated three research queries (RQ) that are based on aim and objectives of the conducted research.

RQ - 1 What is the structural framework of the developed online knowledge based clinical decision support system (KBCDSS)?

RQ - 2 What are the significant features of the proposed system that makes it superior to existing CDSSs?

RQ - 2-a Why the proposed system pursues or follows the steps of KDD process?

RQ - 2-b What is the need of multiple disease oriented framework?

RQ - 2-c Why there is a need to construct web based model?

RQ - 2-d How does the proposed system performs the knowledge acquisition task for mining the viable knowledge?

RQ - 2-e What techniques are employed for the construction of knowledge base (KB) and its representation?

RQ - 2-f How efficient the inference mechanism for concluding and interpreting results?

RQ - 3 How does the problem of human computer interaction is being resolved in our research?

1.5 Research Contribution

The research work done in this dissertation contributes to variety of areas e.g., artificial intelligence (AI) in medicine, diagnostic decision support systems (DDSS), intelligent computing (ICs), and medical informatics (MI).

• To proposed a framework of online Knowledge-based clinical decision support system (KBCDSS): The KBCDSS is a multiple disease diagnostic system which inaugurates the concept to gather medical practitioners of diverse medical fields over one platform through web from where they can check and verify their findings

5 about the patients. The proposed framework of KBCDSS has a distinct structural design that ensures its proficient functioning.

• Pursuing the steps of KDD Process in KBCDSS: KDD process works together with medical domain to extract out viable knowledge that assists medical practitioners in their clinical practices. In KBCDSS, we have followed the pattern of knowledge data discovery (KDD) process for mechanized and useful mining of knowledge. This will make understood the structural hierarchy of the proposed system that is what ways are adopted to extract out the viable set of knowledge that are prerequisite in diagnosis phase.

• Proposed knowledge acquisition algorithm : Knowledge acquisition task is carry- ing out by performing data pre-processing steps to acquire knowledge. To facilitate knowledge acquisition process we have develop an algorithm to gather medical data from different sources (like .csv files .xls) files and which consequently expand knowl- edge base.

• Construction of Knowledge base and its representation : Database management systems (DBMS) is used as knowledge representation (KR) scheme and for the construction of knowledge base (KB). We have proposed an algorithm for knowledge base construction and its representation that is used to store the knowledge in KB that is a part of relational database system.

• Proposed Reasoning Techniques : Implements data mining algorithms namely case based reasoning and support vector machine as an inference mechanism in KBCDSS.

1 Case Based Reasoning Approach: Performed detail study of Case-based rea- soning technique and its cycle.

– In Case Retrieval Phase, similarity algorithm (SA) is proposed for the accomplishment of an efficient retrieval of cases from case repository.

– In Case Reuse Phase, reinstantiation strategy of case adaption is being used for adapting suggesting way out of current problem.

– In Case Revise Phase, proposed solution of current problem is revised by medical experts. Concept of group decision making is implemented in this phase.

6 – In Case Retain Phase, revised solution is then stored into the case reposi- tory as a new case.

2 Support Vector Machine (SVM): If similar cases are not found in case library during case retrieval phase of CBR technique, then we have applied SVM technique to further continue the inference mechanism.

3 Proposed New Hybrid CBR Cycle: A new hybrid CBR cycle is proposed as a hybrid reasoning approach to deduce more accurate conclusion and inferences.

• Perform Data Analysis on Breast Cancer medical data set from UCI machine learn- ing repository and implements these medical data set on our developed system.

• Graphical User Interface: Design user friendly web environment that medical prac- titioners input medical cases of the patient into the system. Through process of input data will generate suitable results over web. The medical practitioners will use resultant data as a knowledge of the current input case. This will be helpful in the process of diagnosis.

1.6 Outline of the Thesis

The thesis comprises seven chapters and details are given:

1 – Chapter 1: Introduction – As an introductory chapter the brief outline of thesis is presented here. A brief background, motivation, research challenges, aim and objectives, research queries and the research contributions are also addressed here.

2 – Chapter 2: Literature Review – It describes the background information of the clinical decision support systems, along with its general model. Also discuss the earlier work on CDSSs and then briefly discuss their types that are Knowledge base system and non-Knowledge base system. Then, we have discussed some recent research projects on CDSSs for diagnosis specific diseases. After that, we have discussed surgical robotic systems and identified some commercially available surgical robotic systems for a specific medical domain. Finally, identified research gaps that exists in the literature review.

3 – Chapter 3: Method and Approaches – This chapter describes the background of the methods which the research is based on. It discusses the two major components of

7 CDSS that are knowledge base and inference engine. For knowledge base, Database Management Systems (DBMS) are employed for the construction of Knowledge base and its representation. For Inference engine, Case-based reasoning approach is used as core methodology in KBCDSS. After that identify metrics for evaluating the classifier performance.

4 – Chapter 4: Development of the Proposed Tool - KBCDSS – This chapter describes the detailed explanation of our proposed online KBCDSS. After that, we have ex- plained the significance of knowledge data discovery (KDD) process in identifying viable knowledge which is prerequisite in making decision accurately. Then we briefly explained the architecture of our proposed system which pursues the pattern of KDD process for mechanized and useful mining of knowledge.

5 – Chapter 5: Proposed Reasoning Techniques – This chapter describes employed rea- soning techniques in our proposed system. After that proposed new hybrid CBR cycle which used CBR as a core methodology and SVM as sub technique to improve the efficiency of inference mechanism.

6 – Chapter 6: Implementation of the Proposed System - KBCDSS – This chapter reveals the detailed discussion on experimental analysis relating to this systems where we have used breast cancer medical data set from UCI Machine learning repository to generate the results. After that shows the measures for evaluating the classifier performance.

7 – Chapter 7: Discussion, Conclusion and Future Directions – Being the concluding part of the thesis, Chapter 7 summarizes the author’s research contributions as well as discussed issues those are given justification of employed or selected methods in this research. It also presents a discussion about the evaluation and results of the system. Further, it presents conclusions drawn from the research work and identify some of future directions that are related to my research work.

1.7 Chapter Summary we have briefly outlines the introduction of the CDSSs and its significance during deci- sion making. Motivation of the research is then discussed in this chapter. we have then

8 identified the major challenges arose while developing CDSS. Then aim and objective of the research is being given. Next section will addressed the research queries and then research contribution in response to these queries.

The next chapter shows the literature review of the proposed research work.

9 Chapter 2

Literature Review

This chapter presents background information of the clinical decision support systems (CDSSs),and briefly explain general model of clinical decision support systems. After that discusses some earlier work on CDSSs. Then we discuss the types of CDSSs, that are knowledge-based CDSSs along with the architecture and Non-Knowledge based CDSSs. Then we have also explained different recent research projects on CDSSs for specific dis- ease.After that, we have discussed surgical robotic systems and identified some commer- cially available surgical robotic systems for a specific medical domain. Finally, concluding this chapter by discussing the research gaps that have been identified from the literature review.

2.1 Background

Due to the rising concerned on clinical data viability, the range of medical knowledge has expanded rapidly as compare to past decade and now it’s a difficult task for medical ex- perts to consider every part of the generated data. The problem of handling large extent of data in a feasible manner originates the concept of computer based diagnosis systems. These computer aided systems are employed to provide clinicians a required bundle of knowledge under a systematic way and limited time span. In AI field aimed to provide more users friendly systems for handling the difficulties in clinical practices. This would consequently extend the employment ratio of decision support systems (DSSs) in medical

10 field. The clinical decision support systems (CDSSs) those are more identical to human reasoning have more significance and are very frequently used in the medical field [Begum et al., 2009]. For this reason CDSS with simple and understandable way of explaining the problems are employed more frequently in the medical field rather than typical and logical CDSS that required computer experts for operational use.

The principal assignments of the clinicians are diagnosis, classification and treatment. The field of AI mainly focuses on providing such systems those are competent enough to execute the tasks of diagnosis, classification and treatment for the clinicians in an easily and acceptable way. Computer based clinical decision support systems developed these days are multi-purposed which used more than one AI techniques. The typical model of a CDSS is designed to incorporate the knowledge base (KB) and the inference engine to produce results for particular situation [Colesca and Zgodavova, 2008]. CDSSs are well renowned due to their knowledge management expertise which exhibits an important role in medical field to maintain and execute the clinical decision making process smoothly [El-Fakdi et al., 2014]. This reveals that computer based decision support systems are developed with an aim to facilitate physicians as well as patients with possible awareness and better care [Musen et al., 2014].

2.2 General Model of CDSSs

The general model of CDSS split into two architectural components that is shown in figure 2.1. One is the knowledge base and the other is the inference mechanism [Kong et al., 2008].

Figure 2.1: The General Model of CDSS [Kong et al., 2008]

11 Knowledge base includes the planned compilation of proficient medical knowledge for the specific medical domain used by the CDSS. Inference mechanism is the combination of different computer-based algorithms required to process inputs for knowledge base. Con- sider figure 2.1, clinical signs, symptoms and pathological reports are taken as inputs whereas diagnostic and therapeutic recommendations are included in outputs of the sys- tems. Using the proposed general model of CDSS, medical practitioners used inputs and through inference mechanism and knowledge base sort out the diagnostic and therapeutic recommendations as an output in decision making process.

2.3 Previous Work on CDSSs

Usage of technology in the field of medical sciences aided medical practitioners (MP) with such technological systems that would give a hand to them during diagnosis and treatment analysis phases. Subsequent discussion outlines the three functional systems that were the beginning of work on CDSSs.

2.3.1 Clinical DSS for Abdominal Pain (System proposed by (De Dom- bal, et al. 1974) ):

There was an attempt devoted to computerize the medical reasoning process through some means, as it was all at that time under ambiguity. FT. deDombal and his colleagues at Leeds University used Bayesian probability theory to develop the computerized DSS. By means of pathological or surgical diagnosis considered as gold standard, they put em- phasis on the significance of obtaining the conditional probabilities employed in Bayesian probability reasoning from high-class data which were collected by gathering information from about thousands of patients [Adams et al., 1986]. In the system proposed by Leeds for diagnosis abdominal pain using sensitivity, particularity and disease occurrence in- formation the signs, symptoms and pathological reports are calculated by making use of Bayes’ theorem, the probability of seven possible explanations for acute abdominal pain namely [Shortliffe and Cimino, 2013]:

• Appendicitis,

• Diverticulitis,

• Perforated ulcer,

12 • Cholecystitis,

• Small-bowel obstruction,

• Pancreatic, and

• Nonspecific abdominal pain

In order to keep the Bayesian calculations convenient, proposed system have developed following assumptions:

1 – The finding of various diagnoses based on conditional independence and

2 – Shared individuality of the seven diagnoses

The development of the proposed system support the diagnosis process of the abdominal aches and necessitate for surgical procedure. The proposed CDSS diagnoses of disease were 91.8 % corrected, on the contrary the medical practitioners diagnoses were correct in 60% to 80% [De Dombal et al., 1974].

2.3.2 Selection of Antibiotic Therapy (System proposed by (Buchanan and Shortliffe 1984)):

An altered methodology of computer based decision support system being embodied into program MYCIN was delivered by Shortliffe and Buchannan, 1984 [Buchanan et al., 1984]. Developers of MYCIN strongly supposed direct algorithms or statistical methods were insufficient for those clinical difficulties or problems where the technical skills were implicit in nature and also the confusions prevailed among the experts regarding diagnosis and treatment inferences. Consequently, researched conduct into solving this problem and research scholars were drawn at an AI field which focused on operating intangible symbols.

Production guidelines were represented in MYCIN as information about infective dis- eases these guidelines contain a pack of information resulting from consultations with field experts. A production guideline is explained as a provisional declaration tells inter- pretations to related conclusions drawn.

I– For making inferences regarding a specific situation the MYCIN platform is used to represent the scheme of rules to be used and the way to chain these rules together.

13 II– These rules are usually formed a rational justification of MYCIN’s cognitive model. Rules that were functional to the existing situation were demonstrated in answer to the questions of users. While these rules were kept as mechanically readable layout and could be shown in English translation.

III– Structure of the program knowledge could be altered by the system experts through eliminating, modifying, or tallying rules.

Patients suffering from infectious bacteria causing blood borne are treated by the sug- gested therapy from the originators of the MYCIN [Yu et al., 1979]. MYCIN was intro- duced to aid the process of diagnosis and treatment of diseases caused by blood infection (i.e. selection of antibiotic therapy). Then this program stretched to work with other diseases caused due to infections [Buchanan et al., 1984].

2.3.3 Delivery of Inpatient Medical Alert HELP (System proposed by (Gardner, Pryor and Warner 1999)):

LDS Hospital in Salt Lake City has introduced this system, they exert on launching a computer based system that is responsible for incorporating the hospital information and named it HELP. The proposed system is deemed to be the relatively successful computer aided patient medical record system. Additionally the proposed system possesses a mech- anism to generate specialized cautions, warnings and reports. If some abnormalities in the patient record are seen the proposed system generate warnings and alerts [Gardner et al., 1999].

HELP is capable enough to produce vigilance signs if there are some abnormalities are seen in the patient signs and symptoms, and the system has enormous impacts for tech- nological advances in this field, by means of applications and tactics that extent almost to the complete array of happenings in the field of bioinformatics [Kuperman et al., 2013]. The competent feature of HELP is that in usual medical domain record system it added a new scrutinizing program and capable to store decision logic in the areas of the system that are called HELP sectors or logic modules. Accordingly, user can utilize specific case from the available patient record, and the conventional pathological reports and plans are routinely printed. This system is presently working in many hospitals of LDS Hospital’s parent health-care enterprise—Intermountain Health Care (IHC).

14 2.4 Types of CDSSs

Typically there are two most important categories of Clinical Decision Support Systems (CDSSs) namely: [Abbasi and Kashiyarndi, 2006; Berner, 2009]:

I– Knowledge-based CDSS in which domain knowledge is acquired from domain experts or medical literature.

II– Non-Knowledge-based CDSS which employs machine learning and different sta- tistical pattern detection methods.

2.4.1 Knowledge based Systems (KBS)

In an AI group, the knowledge based system (KBS) is consider to be the major family member. Through the availability of innovative computing services and other means, con- centration is now moving towards more and more challenging assignments that involves intellectual skills. People as compare to before are now more knowledge oriented and can only rely on authenticated sources of information to make decisions. This is what makes the chance of inaccuracy at its minimum level and increase the reliability of decisions at its utmost level. The proposed models of knowledge based systems turns to be an expert opinion guide for making decisions accurately in a limited span of time without the constraints of any boundaries. Hiring experts for making inferences on the basis of facts and figures is both the time and money consuming and considered to be an item of cost during an assignment. To overcome this problem the knowledge based systems are served to be an ideal alternate by permitting its users to perform at maximum levels and encouraging consistency.

KBS is considered to be a dynamic knowledgeable information system with the compe- tency to provide solutions and inferences to its users. These systems are capable enough to understand the under process information and can generate decisions and inferences based on that knowledge [Akerkar and Sajja, 2010]. KBS are extensively acceptable in the fields where there is predominance of knowledgeable facts and figures rather than operations on an ordinary data and necessitate heuristics and logical expertise to gen- erate case specific conclusions. According to Ackoff’s, data is simply a raw item which itself has no significance at all however knowledge is the definite collection of useful and

15 viable information required in handling a certain situation [Ackoff, 2010]. Medical field integrated both the data and knowledge in balanced way as it uses domain knowledge and data for diagnosis, classification and treatment of the diseases. This ratio of data and knowledge could vary with the variation in situation [Pandey and Mishra, 2009]. Diagno- sis, classification and treatment are the main areas of medical field where these systems are employed. The best suited inference techniques in KBS are case base reasoning, rule based reasoning and modal based reasoning [Pandey and Mishra, 2009].

2.4.1.1 Structure of Knowledge based Systems (KBS)

Typical structure of knowledge based systems comprises of well-maintained knowledge base and a searching mechanism called inference engine(IE). Knowledge base is the com- pilation of structured and non-structured information in a systematic way. Inference engine with solid reasoning capabilities is responsible in deriving conclusions and infer- ences.

Figure 2.2: The Basic Structure of Knowledge-based System [Subbaram Naidu, 2006]

The Basic Structure of Knowledge-based System are divided into four parts that is shown in figure 2.4.1. The knowledge base (KB), database (DB), inference engine (IE) and the graphical user interface (GUI) of the system [Subbaram Naidu, 2006].

i – Knowledge base: This component includes complete knowledge and information regarding the specific domain that would use during the problem solving process.

ii – Database: All the information that the KB components attain from the outer foun- dations will be then accumulate in database. Afterward the accumulated informa- tion in the database match with the view points of the decision making process.

16 iii – Inference Engine: KBS mainly work done through inference engine. As this com- ponent of the knowledge based system supply the methodology or methods of inter- pretations regarding the information accumulated in to the database or knowledge base to generate inferences and conclusions.

iv – Graphical User Interface: It is a graphical interface environment in which the clinician inputs the data and view the result in the form of text as well as graphical form.

2.4.2 Non-Knowledge based Systems

Non knowledge based system do not employ or make use of knowledge base in their workings. These systems simply execute its functioning by means of machine learning techniques of AI which permit computers to acquire knowledge from previous experiences and find trends in medical data [Berner and La Lande, 2007]. This strategy will eliminate the need of maintaining rules and expert’s opinions. One of the drawbacks of employing these systems is that they do not explain the reasoning of their conclusions and inferences and act as the “black boxes”. This is what makes the use of non-knowledge based systems less popular and in fact at its minimum by medical experts. However they can be consider as viable in post diagnosis of diseases for advising patterns to clinical experts to take a look on the issue in more detail and depth. A very limited list of symptoms are required in operating with non-knowledge based systems in contrast to knowledge based system that requires wide range of diseases [Berner and La Lande, 2007].

The famous methods that are used in non-knowledge based systems are support vector machine (SVM), genetic algorithm (GA) and artificial neural networks (ANN) [Wagho- likar et al., 2012].

2.4.2.1 Support Vector Machine (SVM)

Support vector machines (SVMs) is defined as a managed non-parametric method for statistical analysis, thus data distribution could not require any underlined assumptions. SVM original design includes the presentation of categorized data sets instances along with the preparation algorithms of SVM that targets to discover hyperplane which splits the data set into distinct predefined figures of classes in a constant approach with avail- able training samples [Vapnik, 2006]. Optimum split up hyperplane mention the decision

17 edge that reduces the extent of misclassification, found during the steps of training.

Analytical learning mentions the frequent procedure of discovering a classifier along with the optimum edge to distinct the training configurations and then to disperse model data in the same pattern.

Figure 2.3: Support Vector as Separating Hyperplane between two Classes [Mountrakis et al., 2011]

Figure 2.3 demonstrates a simple case of two class distinct ordering problem under a two dimensional input area. A significant simplification facet of SVM is that in order to sepa- rate hyperplane, not all offered training samples are required to be used in the explanation and design of the splitting hyperplane.The divisions of points that on the support vector define by a margin merely describe the hyperplane of maximum margin. The main feature of support vector machine is slow training but accuracy is high owing to their ability to model complex nonlinear decision boundaries (margin maximization)[Mountrakis et al., 2011]. SVM is used for classification and prediction both [Han et al., 2011].

The foremost significant feature of SVM is its capability to simplify from an inadequate amount or worth of training sets available. On comparing with other alternate proce- dures like back spread neural networks, SVM could produce equivalent level of accuracy by employing lesser training models. Support vector is famous due to its competency in generating fruitful results by taking in consideration fewer training samples. This com- petent feature has ascertained to be worthwhile when implemented because reaching to a

18 right decision in medical diagnosis is usually crucial task. SVM is one of the most popu- lar approaches that are used by researcher in health-care field for classification. We have used SVM as a sub technique for inferencing in KBCDSS. The detailed overview and its mathematical description will be discussed in Chapter 5, Section 5.3.

2.4.2.2 Genetic Algorithm (GA)

GA as a non-knowledge based system has endeavor to integrate the concepts of natu- ral progressions. These algorithms right after their innovation [Holland, 1975] have been engaged in an extensive range of knowledge and issues reconciliation regions[Ross and Corne, 1994]. These system are responsible in providing algorithm based framework for manipulating the progressions of modification and assortment in a stimulated manner.

The GA are typically began with the arbitrary population of encrypt application, known as Chromosomes. By means of recombination procedure and transfiguration operatives the population advances towards ideal results. Producing the ideal results is not certain and the task is therefore to design such genetic procedure that ensures the maximum likelihood of producing prerequisite results.

The prime step to execute the functioning of this system is to estimate the fitness of every applicants solution available in the current set of population, and then to choose the most suitable applicant solutions to perform as the parentages of following peers of applicant solutions. Once being choose for the process of reproduction, parentages are supposed to be rejoined and mutated for generating peers or offspring’s [Godefroid and Khurshid, 2004].

It is more likely to congregate towards the globalization as it concurrently estimates several points available into the sample space [Hanirex and Kaliyamurthie, 2014]. On comparing to different predictable exploration algorithms with the genetic algorithm, there is the competency in genetic algorithm to automatically acquire and accumulate the pre-requisite set of knowledge regarding the sample space while pursuing its search- ing process and employing optimization technique to handle the complete search process [Da Silva et al., 2011].

Genetic algorithms are professionally applied in several areas in business. More specifi-

19 cally in scheduling, allocating budget expenditures, client feed backs for promotion are the especial regime to apply genetic algorithm to facilitate the decision making process [Linoff and Berry, 2011; Delen, 2014].

2.4.2.3 Artificial Neural Network (ANN)

ANN formally derived from neuro-biological prototypes that are composed of extremely unified and interrelated dispensation divisions. The first wave of attentiveness in ANN appeared right from the overview of streamlined neurons by Pitts & McCulloh [McCulloch and Pitts, 1990].

The foremost significant feature of ANN is its competency in picking up and learning. It resembles to human intelligence while need to learn through examples and vigorously altered themselves to be fitted into the presented data [Cox and Dean, 2014].

Artificial neurons are the elementary components [Maind and Wankar, 2014]. In an updated calculated model of nodes, the outcomes of synapses have been presented by means of associated weights that restrain or control the connected input signals, whereas the deviated features revealed by neurons are presented through a relocation task. The knowledge proficiency of node is being attained by altering the weights according to se- lected algorithm [Abraham, 2005].

As a consequent ANN have been engrossed numerous researchers and arose as most fa- mous application in recognizing patterns and for classifying purpose [Sivagaminathan and Ramakrishnan, 2007].

2.5 Recent Research Projects on CDSSs for a Specific Dis- ease

Recent studies show that CDSSs are the dynamic knowledgeable information systems invented with an aim to advice the medical experts during diagnosis by taking into con- sideration different attributes of the patient [Fong et al., 2013]. Different approaches and parameters are used to deal with different diseases. Different application oriented CDSSs have designed for diagnosis the specific disease shown in table 2.1.

20 Table 2.1: Specific Disease Oriented Clinical Decision Support Systems (CDSSs)

Author System Purpose of Application Techniques (year) Name the System Domain Used

[Sharaf-El-Deen Breast cancer, MDSs Diagnosis CBR, RBR et al., 2014] Thyroid disease

[Guessoum Chronic pulmonary RESPIDIAG Diagnosis CBR et al., 2014] disease

[Tomar and CHLD- Chronic heart Diagnosis CBR, MBR Singh, 2013] MMCDSS diseases

[Neshat et al., Clinical Diagnosis Hepatitis disease PSO, CBR 2012] DSS

[Lin and ANN, AHP ILDM Diagnosis Liver disease Chuang, 2010] and CBR

CBR, NN [de Paz et al., Expression Diagnosis and Cancer disease and 2009] CBR Classification Statistics

[Begum et al., IPOS Diagnosis Stress Diagnosis CBR, FL 2009]

[Ahn and Kim, Clinical Diagnosis Breast cancer CBR, GA 2009] DSS

[Nicolas et al., Clinical Diagnosis and Melanoma disease CBR, RBR 2009] DSS Classification

[Töpel et al., Clinical Planning and Inborn Metabolic CBR 2007] DSS Diagnosis disease

[Kurbalija et al., Clinical Multiple Sclerosis Diagnosis CBR 2007] DSS (MS)

Clinical Children with [Chang, 2005] Diagnosis CBR DSS development delay

Continued on next page

21 Table 2.1 – continued from previous page

Author System Purpose of Application Techniques (year) Name the System Domain Used

[Lorenzi et al., Fraud Detection in SISAIH Diagnosis CBR 2004] Health care

2.5.1 Medical diagnosis systems for breast cancer and thyroid diseases [Purpose: Diagnosis]:

The proposed system is based on hybrid reasoning approach which incorporates the tech- niques of case based reasoning (CBR) and rule based reasoning(RBR). The technique of CBR is used to enhance the level of accuracy in case retrieving process whereas RBR tech- nique is employed to generate adaptation rules that applied in case adaptation process. Once the new case is being solved, case repository will be extended which consequently up- date reasoning and adaptation rules. The execution of proposed system was implemented on diagnosis breast cancer and thyroid diseases [Sharaf-El-Deen et al., 2014].

2.5.2 RESPIDIAG - Medical Diagnosis System for respiratory disease [Purpose diagnosis]:

This system is constructed for diagnosis the critical respiratory diseases effectuated by tobacco. System employs the technique of case based reasoning and put together ex- periences of medical consultants of Dorban Hospital’s pneumology department(Annaba, Algeria). A serious problem arose about the case-based reasoning strategy is the absence of attributes values in majority of cases. The developed system mainly handles the prob- lem of these missing values. This system focused on retrieving process of CBR technique for retrieving alike cases on the basis of input attributes values [Guessoum et al., 2014].

2.5.3 CHLD-MMCDSS [Purpose: Diagnosis]:

“Multimedia Based Clinical Decision Support System for Diagnosis of Chronic Heart Dis- eases (CHLD-MMCDSS)” was launched with an aim to increase the Quality of Life (QOL) of manufacturing/operative personnel undergoing from semi-structured/ unstruc- tured Prolonged Heart Syndromes. For diagnosis the diseases the proposed system em- ployed case base reasoning and model based reasoning as an inference mechanism. CHLD-

22 MMCDSS aids clinicians in making managerial decisions by relating the efficiency of many other Multi Futuristic Medical Decision Parameters (MFMDP) also helps in making poli- cies and recognizing the diseases of heart [Tomar and Singh, 2013].

2.5.4 Decision-making system for diagnosis hepatitis disease [Purpose: Diagnosis]:

The system developed for diagnosis hepatitis disease is based on the integration of two techniques Particle swarm optimization (PSO) and (case based reasoning)(CBR). Initially, case based reasoning technique is viable for the data preprocessing and then allocate weights vector to the features or attributes of the hepatitis data set values. After that particle swarm optimization model is applicable to accumulate a decision making system build on the basis of recognized diseases and extracted features. This system could provide 93.25% accurate results of the hepatitis disease [Neshat et al., 2012].

2.5.5 ILDM [Purpose: Diagnosis, Classification]:

ILDM is an intelligent liver diagnosis model developed with an aim to make differentiation among normal liver and unhealthy liver using ANN technique. Moreover, it incorporates case-based reasoning (CBR) technique with the analytic hierarchy process (AHP) for the purpose of diagnosing different categories of liver diseases. The proposed model was presented to facilitate medical experts in diagnosis liver diseases more accurately [Lin and Chuang, 2010].

2.5.6 ExpressionCBR [Purpose: Diagnosis, Classification]:

This system is capable in classifying the patients suffering from leukemia with the help of available data and aids in the diagnosis of multiple cancerous cells. In order to solve the dimensionality issue arose I data set system applies data filtering algorithm .This system has employed clustering algorithm to fasten the process of classification [de Paz et al., 2009].

2.5.7 IPOS [Purpose: Diagnosis]:

This system is a case-based decision support system developed to aid medical practitioners while they are evaluating stress patients. Stress diagnosis depends on finger temperature sensor signal [Begum, 2009]. This system uses calibration phase for generating individual stress profile. Case retrieval phase of the CBR technique is applied to retrieve the preced-

23 ing related temperature profiles. By integrating fuzzy technique with CBR systemto deal with uncertainty, vagueness in experts reasoning and ambiguity of feature values. This system attempts to improve the reliability of diagnosis and decisions [Begum et al., 2009].

2.5.8 CBR System for diagnosis breast cancer patients [Purpose: Diag- nosis]:

CBR system proposed to diagnose the breast cancer via digital images. Performance of the system improved by employing genetic algorithm (GA). This system also optimizes the number of neighbors, instance selection and feature weighting which respectively combines by employing GA [Ahn and Kim, 2009].

2.5.9 Diagnostic System for Melanoma Patients [Purpose: Diagnosis, Classification]:

This system is developed with an aim to help medical experts for the diagnosis of melanoma disease. Case reuse phase of case based reasoning technique is being employed in this sys- tem. System employs two techniques for melanoma-diagnosis that are based on images in the domain for diagnosis. To improve the classification performance, applied pre-processed rules are applied up on the integrated results of the images. For reliable diagnosis result used two independent CBR classifiers are used. The pre-processing algorithm produces set of characteristics form the data set of melanoma disease and then results from the two individual CBR modules are combined using the rules [Nicolas et al., 2009].

2.5.10 CBR System for diagnosis for inborn metabolic diseases [Pur- pose: Diagnosis, Planning]:

CBR system is applied for the diagnosis and therapeutic planning relate to inborn metabolic diseases. System based on two parts: one is the problem part and the other is the solution part. Problem part include cases that are enclose with symptoms, clinical findings, de- velopment and molecular test results while the solution part contains diagnosis, therapy, diet and drugs. This system mainly focuses on the case retrieval phase of CBR technique and reduces the computational time for retrieving the similar cases using the pre-selection of cases from the case repository [Töpel et al., 2007].

24 2.5.11 Case-based decision support system used for diagnosis multiple sclerosis disease [Purpose: Diagnosis]:

The proposed decision support system developed to diagnose multiple sclerosis disease uses CBR technique to execute its working. The major feature of the proposed system is to assigned weights automatically to each attributes value of the case. The proposed system is very helpful for the new clinician to take decision during their diagnosis and as well as experienced clinicians for the confirmation of their decision [Kurbalija et al., 2007].

2.5.12 Clinical DSS for Developmentally Delayed Children [Purpose: Diagnosis]:

CBR technique is being used in constructing a clinical DSS for screening the developmen- tally delayed children. Screening would help in identifying the symptoms that indicate delays in the developmental status of children. CBR technique is employed to enhance the efficiency of this system. The proposed system considers the sensory and cognitive development for the diagnosis of developmental delay in children [Chang, 2005].

2.5.13 Hospital Admission Authorization System (SISAIH) [Purpose: Diagnosis]:

Hospital admission authorities in Brazilian public health system uses this decision-support tool as a guidance or aid in decision making process. System performs the managerial tasks in hospitals like admission of a patient issues related to billing errors and other medical procedures [Lorenzi et al., 2004].

2.6 Surgical Robotic Systems

Hosting the concept of robotic surgery permit physicians to achieve more accurate results in performing complex surgical processing that conventional methods so far couldn’t im- plement soundly. Typically a slight intrusive surgery is being linked to robotic surgery that is done by means of small slits. US and Europe are more frequent users in embracing for treatment of variety of conditions [Dutta, 2015].

25 Computer assimilated medical operating systems as an innovative surgical robots are revolutionized and delegated to be the most effective notion. Surgical system includes the robotic manipulator as the substantial component which is account for patient’s authenti- cation and follow up, foregoing planning that is based on medical images, and registration preceding to operative measures and a compilation of mechanically aided and manually handled tools for executing the plan [Ruff and Ogilvie, 2013].

The recent overview about surgical robots’ assistance during surgeries proposes substantial breakthrough in conventional surgical techniques. The quality and upshots of the earlier surgical procedures being upgraded by integrating the concept of high-tech innovations with clinical domain. For more than a decade innovations based on robots breakthroughs in academic world and industry to accompanied the manual procedures. Applying these systems on training sessions have shown profound consequences.

Computer automation mockups can empower some of the training sessions based on surgical trials to be executed virtually without being harmful or risky to environment. Moreover these computerize devices would hopefully permit to efficiently track the surgi- cal learning progression.

Medical practitioners and software experts collaborate to innovate the surgical robotics systems thus the field of robot surgery has become an evolving and mesmerizing field of medical sciences. Though the progression of surgical robots have been increased to a larger extent but still they couldn’t replace the diagnosis of doctors.

2.7 Surgical Robotic Systems for a Specific Domain

The advent of medical robots specifically in surgical domain couldn’t be possible with- out combining the high-tech strategies (motor, control theory and material), progression in clinical imaging process (3D ultrasound, higher resolutions, and magnetic resonance imaging) and the approval from both the surgeons and patients to go for the laparoscopic methods and robotic surgical aids.

Novelty in practices of these surgical robots are more frequently innovated, being the

26 preliminary phases of any technology based revolution [Beasley, 2012].

Subsequent discussion will highlight some earlier proposed medical robotic systems shown in table 2.2 that are used in the surgery more specifically for a particular domain.

Table 2.2: Computer Assistant Robotic Systems for a Specific Medical Domain

Application Company System Functions Domain Name Name

Used in Biopsy, Deep brain Renishaw NeuroMate Stimulation, Radiosurgery and Neuroendoscopy etc. Used in guiding needles for Neurological Prosurgics Pathfinder biopsy, guiding drills to make burr holes. Deformity Corrections, Biopsies Mazor Renaissance and Electrode placement Robotics procedures.

CUREXO ROBODOC Total hip & knee replacement Tech. Cor. MAKO RIO Plantation of medial & lateral Surgical Robotic unicondylar knee component, Cor. Arm Patellofemoral arthroplasty Praxim Automated cutting guide for Orthopedics iBlock Inc. total knee replacement Blue Belt Used Intraoperative planning for Navio PFS Tech. Unicondylar knee replacement Provide Surgical planning, Stanmore Stanmore incorporate 3D model Used for Implants Sculptor unicondylar knee replacement

Continued on next page

27 Table 2.2 – continued from previous page

Application Company System Functions Domain Name Name

Used adult & pediatric robotic surgery procedures in Urology, Intuitive general laparoscopic, Surgical Da Vinci. non-cardiovascular thoracosopic Inc. and thoracoscopically-assisted General cardiotomy procedures Laparoscopy Suitable for laparoscopic Freehand FreeHand procedures and used different 2010 Ltd. approaches etc. Eyetracking to control SOFAR Telelap endoscopic view and to enable S.p.A. ALF-X activation of various instruments.

2.7.1 Neurological

In case of brain surgery the physicians are supposed to access suppressed target which is being covered by number of mild tissues. This scenario could be more easily done through the capability of surgical robots as they can more accurately tracked the motions that are based on medical image processing [Najarian et al., 2011; Haidegger et al., 2008; Nathoo et al., 2005]. In 1985 the first ever robot surgery conducted was the brain biopsy by means of Computed tomography scan (CT scan) along with stereotactic frame [Kwoh et al., 1988].

Some of the commercially available neuro-robotic surgery systems are discussed below.

2.7.1.1 NeuroMate (by Renishaw)

The NeuroMate (by Renishaw, previously by Integrated Surgical Systems, previously by Innovative Medical Machines International) has the Conformite Europeenne (CE) bench- mark and is presently employed in the FDA clearance process [Varma and Eldridge, 2006]. In addition to the procedure of biopsy, this system is promoted for profound stimulus for

28 brain, stereotactic electroencephalography, trans-cranial hypnotic stimulus, radiosurgery and neuro-endoscopy.

Figure 2.4: NeuroMate by Renishaw

2.7.1.2 Pathfinder (by Prosurgics)

Food & Drug Administration (FDA) have issued the clearance certificate to Pathfinder (Prosurgics, formerly Armstrong Healthcare Ltd.) for the purpose of neurosurgery in 2004 [Morgan et al., 2003]. Employment of this system enables the surgeons in specifying the objective path relating to medical images preceding to operative measures along with this theses robots are efficient enough to guides the instrumental motion with an accuracy of even sub millimeters [Deacon et al., 2010]. Testified usages of this system are guiding needles used for the biopsy purpose and guiding drills for marking burr holes [Brodie and Eljamel, 2011].

Figure 2.5: Pathfinder by Prosurgics

29 2.7.1.3 Renaissance (by Mazor Robotics)

The system Renaissance has been approved by the FDA in 2011 and achieved the CE mark for the final surgery and brain operations in 2011 [Joskowicz et al., 2011]. The system comprises of two parts one is the robot that is capable to straightaway supports spinal cord whereas the other part is the guideline to use the software for the purpose of planning while executing different functioning like distortion adjustments, smooth biopsy proce- dures, mild intrusive surgeries and electrode adjustment processes. Researches shown that the improved transplant accurateness and the provision of confirmation that the Spine Assist system could possibly permit significant range of transplant engaged percu- taneously [Devito et al., 2010].

Figure 2.6: Renaissance by Mazor Robotics

2.7.2 Orthopedics

The probable significance of robot surgeries in the orthopedics is exact and accurate bone resection [Yang et al., 2010; Lang et al., 2011]. By means of accurate bone resection the robot surgical systems can recover the alignment of transplant with bones and hence im- proves the contact region among the transplant and bone which may together enhances the efficient outcomes and transplant endurance [Davies, 2000].

So far targets of the orthopedic robots are the hip and knee joints to be replaced or resurfaced. These systems are initially required to fix bones at their places and uses bone screws to confine surgical region.

Some of the commercially available orthopedic robotic systems are discussed below;

30 2.7.2.1 ROBODOC (by CUREXO Technology Cor.)

CUREXO Technology Corporation (formerly Integrated Surgical Systems(ISS)), had of- ficially introduced this system that has achieved the FDA clearance for complete hip replacement in 1998 and for the entire knee replacement in the year 2009 [Schulz et al., 2007] and received the CE mark in 1996 as the first ever human assistance robot in Total Hip Arthroplasty(THP).

Figure 2.7: Robodoc by Curexo Technology Corp.

ROBODOC R system is divided into two major parts:

• ORTHODOC R

• ROBODOC R

ORTHODOC is the planning and organizing workstation that worked in preoperative phases and ROBODOC is the surgical service assistant. ORTHODOC transforms the patient’s joint CT scan into a 3D bone spitting image, that can be easily handled by the physician to overview the bone and joints features, consequently permitting for best prosthetic choice and exact alignment. physicians choose the optimal size, type and sort of femoral stem prosthesis by using ORTHODOC digital library of prosthetic images. Preoperative planning in a precise manner to every patient is being adopted in this virtual operation which have been then forwarded to ROBODOC, the functioning part of the system that implements the plan by refining the bone with an accuracy of even sub millimeters, hence fixing the bone to accept the prosthetic transplant with an accurate fitting. Revising some earlier surgeries could also be done through ROBODOC due to its competency in bone cement removal [Spencer, 1996; Bargar et al., 1998; Nishihara et al., 2004; Schulz et al., 2007].

31 2.7.2.2 RIO Robotic Arm (by MAKO Surgical Cor.)

System RIO robotic arm used to transplant unicodylar knee joints and also for patellofemoral arthroplasty had received the FDA clearance in 2008 [Pearle et al., 2009; Roche, 2014].

Figure 2.8: RIO by MAKO Surgical Corp.

This system is not entirely autonomous like the other surgical robotic systems, it requires the manual assistance from the surgeon to perform its task. Physician and RIO both can simultaneously grip the operating tool for moving the surgical region. This RIO robot arm is developed to be of less inertia and less friction, thus the physician can easily operate the tool [Rosen et al., 2011].

2.7.2.3 iBlock (by Praxim Inc.) iBlock the robotic collaboration is an autonomous spitting aid tool for total knee replace- ment and had received the FDA clearance in 2010 [Plaskos et al., 2005].

Figure 2.9: iBlock by Praxim Inc.

The iBlock is straightway fixed to the bone, avoiding any distortion generate due to the motion among robot and bone the physicians manually employs planner cuts that relate to preoperative surgical plans. [Koulalis et al., 2011] report concentrated surgical

32 duration and enhance the amended accuracy associated with untraced triangulation of cutting blocks.

2.7.2.4 Navio PFS (by Blue Belt Tech.)

The Navio PFS (by Blue Belt Technologies) with CE mark 2012 is independent of CT scan for replacing unicodylar knee it alternatively employs planning used during the surgical trajectories [Brisson et al., 2004; Brisson, 2008]. Throughout the procedure the drill tool is being tracked and in case it leave the projected path drill bit would then need to be draw back.

Figure 2.10: Navio PFS by Blue Belt Tech.

2.7.2.5 Stanmore Sculptor (by Stanmore Implants)

This system is quite similar to the earlier proposed system RIO with vigorous controls to retain the physician in pre-planned workstation [Yen and Davies, 2010]. The concern’s "Savila Row" robot system modifies a unicodylar knee transplant for patients that collab- orated with 3D prototype for transplant in surgical planning edge and employs vigorous constraints with Stanmore Sculptor to confirm accurate adjustments of the bone exteriors. FDA has not approved the system with clearance yet but still successfully employed into the Europe for more than a decade.

Figure 2.11: Stanmore Sculptor by Stanmore Implants

33 2.7.3 General Laparoscopy

The camera technology has emerged in the last of 1980s, so far improved the efficiency of laparoscopy, in that more than one tiny slits are used to reach the surgical region by means of camera and tools [Harrell and Heniford, 2005].

This synergy reduces the patient trauma to a significant extent as compare to conventional surgical procedures and thus ensures the speedy recovery but involves the complications in surgery [Kuo and Dai, 2009; Dogangil et al., 2010; Gomes, 2011].

Following are some of the systems discussed.

2.7.3.1 da Vinci. (by Intuitive Surgical Inc.)

Intuitive Surgical incorporation had developed the da Vinci and received the FDA clear- ance for employing in both the adults and pediatric robotic operation in the following fields: [Dutta, 2015].

• Urological surgeries

• General laparoscopic surgeries

• General non-cardiovascular thoracosopic surgeries

• Thoracoscopically-assisted cardiotomy procedures

Figure 2.12: Da Vinci by Intuitive Surgical Inc.

The da Vinci is intended to assist in the control of several endoscopic instruments, in- cluding: [Estey, 2009]

• Rigid endoscopes

34 • Blunt and sharp dissectors

• Scissors

• Scalpels and

• forceps

2.7.3.2 FreeHand (by Freehand 2010 Ltd.)

The surgical robot Freehand will serve as next generation endoscope holder. On comparing to earlier developed robotics its arm is more compressed and format at an ease with lower cost. Surgeons used moderate head nods to control endoscope motions that are tracked an optical system. It receives the FDA clearance in CE mark in 2009.

Figure 2.13: FreeHand by Freehand 2010 Ltd.

2.7.3.3 Telelap ALF-X (by SOFAR S.p.A.)

Telelap is developed with an aim to compete the da vinci and had achieved the CE mark in 2011. It’s a four armed surgical robotic system [Stark et al., 2012]. Enabling activation of different instruments and to control endoscopic outlook system possess an eyetracking competency. The surgical robotic telelap in response to da vinci’s features can be more proficiently move the base of manipulator for about 80 cm from the patient and have an accurate tactile-sensing ability.

Figure 2.14: Telelap ALF-X by SOFAR S.p.A.

35 2.8 Research Gap

The critical review of the literature on already existing CDSSs concluded that a wide range of computerized decision support systems have been introduced in previous four decades of technological innovations in the medical field. These systems have shown significant potential for influencing the patient care. Still, after the 40 years of their progression, none of these CDSSs are frequently employed into the medical field due to their complex nature and it is difficult to work through these systems without the help of any system expert [Berner and La Lande, 2007]. There are number of problems arose to execute these systems in medical field which include the selection of field or targeted domain, maintaining and construction of knowledge base, algorithms applied in making inferences, problem of human computer interaction and the difficulty to validate the so- lution [Miller and Geissbuhler, 1999].

[Kawamoto et al., 2005] have identifies the four competent features of a successful CDSS:

1 – Clinical work flow should have an autonomous endowment of decision support.

2 – Should provide recommended suggestions rather than just valuations.

3 – On time decision support at the time of patient care.

4 – Computerized form of decision supports.

In order to attain above mentioned features identified by Kawamoto and his associates, a CDSS should possess following competencies:

• A well organized structured and all-inclusive collection of medical domain knowl- edge.

• Systematic knowledge representation scheme to highlight the complete work flow in an appropriate manner.

• Diagnosis system with strong reasoning and inference capabilities to generate case specific conclusions and results.

• User friendly environment that would help clinicians to interact easily with the system.

36 The problem of constructing a suitable database which is capable enough to accumu- late patient’s medical information and declarative and workable knowledge could not be challenging to overcome through the rapid innovations and technological progressions. Yet, suitable manner of knowledge representation and maintaining the knowledge base in systematic way, appropriate reasoning technique as an inference mechanism, stress free user interface and the multiple disease oriented web base system are the main challenges [Kong, 2011]. Keeping in view the preceding detailed discussion about CDSSs that are already existing following research gaps have been identified.

1 – Current CDSS should be capable to deal with multiple diseases along with web base model.

2 – There is necessitating developing a CDSS that pursues the more enlightened knowl- edge representation scheme which is competent to store and represent structural data in an organized manner.

3 – There is a need to employ more refined reasoning technique as an inference mechanism which can explain the situation significantly.

4 – Current CDSS should be capable to support group clinical decision making process to enhace the level of accuracy in decisions.

5 – There is a requirement of user friendly GUI environment so that clinicians can easily access to the system.

This research work is carried out with an aim to develop an online knowledge base clinical decision support system which has the competency to fulfill above mentioned research gaps. The detailed description of the proposed online clinical decision support system (KBCDSS) is presented in chapter no 4. In chapter 5, we have discussed the reasoning techniques that are applied to execute the functioning of the system.

2.9 Chapter Summary

Chapter 2 comprehensively provide the background information of earlier proposed CDSSs. General model of CDSS is then explained along with some earlier proposed CDSSs that were the beginning of work on CDSS. Then briefly explained the types of CDSS that are

37 knowledge-based and non knowledge-based systems. Some recent research projects on CDSSs for specific disease are also explained in this chapter. After that, briefly explained surgical robotic systems and discussed some commercially available surgical robotic sys- tems for a specific medical domain. Finally mentioned the research gap which have been identified from the literature review.

The next chapter will explain the method and approaches employed in our proposed system.

38 Chapter 3

Method and Approaches

This chapter reveals the background of the method and approaches on which the conducted research work is based on. It discusses the two major components of CDSS that are knowledge base and inference engine. Database Management Systems are employed for the construction of knowledge base and its representation while as an inference engine, case based reasoning approach is used as a core methodology in our proposed Knowledge based Clinical Decision Support System (KBCDSS). The theoretical overview of the methods will significantly give a better understanding of the following chapters.

3.1 Introduction

Recent development in launching the CDSSs aim to developed a computer based programs that may simulate human intelligence [Berner, 2009]. Typically the knowledge based sys- tems (KBS) are divided into three components, knowledge base (KB), inference engine (IE) and a mechanism that users adopt for interaction have shown in figure 2.1.

Knowledge base (KB) is the comprehensive compilation of target domain in a systematic order which will be responsible in effective functioning of CDSSs. An inference engine (IE) is liable to generate required results and conclusions by applying different reasoning and inference techniques. Whereas the user interface is as important as other components of the CDSSs for the purpose of human computer interaction [Berner, 2009; Musen et al.,

39 2014].

In our proposed online knowledge base clinical decision support system (KBCDSS), DBMS are used for the construction of KB and to represent that knowledge. Being an inference mechanism case base reasoning (CBR) technique is applied as a core methodology in our proposed system and when the solution is not available in knowledge base (KB) or case repository as a sub methodology support vector machine (SVM) is incorporated to predict solutions of new input problem case. The group clinical decision making (GCDM) is used for the validation of new predicted solution or of new adopted solution for the current input case problem.

Beside, used some of the measures for assessing how good or how "accurate" the per- formance of the classifier. The classifier evaluation measures applied during the system evaluation are summarized as: "Accuracy (also known as recognition rate), Error Rate or Mis-classification Rate, Sensitivity (or recall), Specificity and Precision"[Han et al., 2011].

3.2 Knowledge Base and its Representation

Well planned and properly structured knowledge base is deemed to be an essential com- ponent of any good CDSSs as it execute the functioning of the system more efficiently than any other decision support system with poor knowledge base. Designing and im- plementation of the CDSSs from the preliminary stages to final stage cautiously plan the construction of knowledge base as it ensures the proficiency of the CDSSs. Thus con- struction of knowledge base and its representation is imperative to accomplish an effective execution of CDSSs [Marling et al., 2014].

In medical domain, knowledge about the patient’s disease and medical record varies ex- tensively which make it difficult to handle. Since the selection of appropriate knowledge representation scheme for constructing a planned knowledge base requires professional expertise. Managing the large extent of clinical domain knowledge is a challenging as- signment and necessitates proficient technical skills to resolve this task.

In the proposed online KBCDSS, there is need to use such knowledge representation

40 scheme that is capable to handle structural knowledge appropriately and construct knowl- edge base. Therefore we have employed database management systems (DBMS) to store and represent the structural knowledge in our proposed online KBCDSS.

3.3 Case Based Reasoning

In the field of AI the current progressions have innovate the concept of case base reasoning approach as a significant and extensively applied technology for problem solving [Marling et al., 2014]. Cognition based model of case base reasoning make it more and more well known in the fields where knowledge is predominant to make decisions. CBR approach dedicated its consideration towards solving a new problem by keeping in consideration the previously stored cases that are more alike from the situation in hand. More specifically, the concept of CBR utilizes understanding gained from problems that are identical or occurred previously and were stored as record in relational databases. These stored cases could be used as reference in solving a new situation or problem case. This concept of CBR approach required maintenance of structured retention of cases that is also termed as case base. The case base is accountable to represent the similarity and means for iden- tifying resemblance between cases.

The CBR system CYRUS, were initially developed by Janet kolodner in 1983 [Kolodner, 1983a,b]. She has represented knowledge as cases and employed the structured memory index. According to [Kolodner, 1992]"In case-based reasoning, a reasoner remembers pre- vious situations similar to the current one and uses them to help solve the new problem". Therefore, the main focus of this approach is towards cognition process that is learning from previous cases or recalling memory to solve the present situation. In the views of [Riesbeck and Schank, 2013]"a case-based reasoner solves new problems by adapting so- lutions that were used to solve old problems". Most of the early developed CBR systems such as CASEY [Koton, 1988] and MEDIATOR [Simpson Jr, 1985] executed on the basis of the system proposed by Janet kolodner.

CBR technique is significantly applied in the field of medical sciences where knowledge is being of primary importance [Begum, 2011]. Cognition model of CBR approach is quite similar to human thinking and also relatively alike to the problem solving approaches in

41 medical domain [Ahmed, 2010]. Medical experts while treating a new patient or dealing with new cases also necessitates making use of similarly associated cases to explain the current situation. For this purpose physicians make comparisons between the stored and new case that are based on the patient’s signs and symptoms. This comparison will help them in extracting out the most similar case to current situation along with its diagnosis. Thus, these past experiences will significantly aid medical experts in solving a new case.

3.3.1 Case

The first and foremost footstep in explaining a CBR system is to briefly overview a formulation of a case. Cases can be described as the part of knowledge considered as experience and plays a significant role in phase of reasoning and amplification [Pal and Shiu, 2004]. According to [Watson, 1998]"cases are a contextualized piece of knowledge representing an experience". Hence cases could be illustrations of things or a fragment of a situation which has been experienced. Generally cases are presented as problem addressed and available remedies for the stated problem shown in figure 3.1.

Figure 3.1: Structure of the case [Begum, 2011]

The portion of case which states the problem is depicting the situation that is being required to be solved however the other portion of the case is the provided solution of the problem stated in the preceding part of the case. More specifically these cases are comprises of the attributes of the diseases with their suggesting diagnosis and treatments.

3.3.2 Case Library

Case base or case library accumulate or stored the previous experiences as the past cases. “The case library, from a cognitive science perspective, is a kind of episodic memory that represents the problem solving experience of our computational entity”[Begum et al.,

42 2013]. In CBR system, we therefore consider case library as a key resource of attaining knowledge shown in figure 3.2.

Figure 3.2: Case Library [Begum, 2011]

So, case library should include representative issues that comprehensively covers the prob- lems in an efficient manner. Suppose the contain cases in the case library are of low quality then it will not generate quality decisions. The case library is capable of accumulating fresh set of knowledge in a dynamic way.

3.3.3 CBR Cycle

CBR cycle is comprises of four steps which was initially introduced by [Aamodt and Plaza, 1994], these steps are respectively case retrieval phase, case reuse phase, case revise phase and case retain phase shown in figure 3.3.

Figure 3.3: Schematic Cycle of CBR [Aamodt and Plaza, 1994]

43 3.3.3.1 Case Retrieval Phase

Case retrieval phase is deemed to be the primary step in the processing of CBR system. Retrieval is considered to be the most vital step as it is responsible in computing similarity in between cases. The situation in hand is termed to be the new case and the experienced cases that are stored in case library are old cases. Cases are compared on the basis of similarity measurement. This comparison made to compute similarity between cases will allow extracting or retrieving similar cases. Similarity measures lies between the ranges 0 to 1, where 0 shows that there is no similarity between the cases and 1 reflects that cases are completely matching with each other. Cases are compared on the basis of similarity measurement. More than one retrieval algorithms are used in the technique of case base reasoning. The widely applied case retrieval algorithms are decision tree and its derivatives, nearest neighbor algorithms. Brief overview of most extensively applied algorithms are discuss below [Pal and Shiu, 2004].

3.3.3.1.1 Nearest Neighbor Algorithm for Case Retrieval

Case retrieval algorithm nearest neighbor selects the retrieved cases that where kept as experiences in case base on the basis of weighted summation of their features which are identical to the current case to be solve. Computational formula of this algorithm for case retrieval is shown in eq. 3.1 .

Pn i=1 Sim(Ci,Si) ∗ Wi Sim(C,S) = Pn , (3.1) i=1 Wi

• C = represents the new problem case or marked case

• S = cases that were stored in case library or case base

• n = cases feature values

• i = particular feature of disease

• Sim = represents the function of similarity among the attributes of new input case and of the cases that are already kept in case base.

• W = signifies the weight of every feature. Importance of features is identified by these weights which are assigned by medical experts.

44 3.3.3.1.2 Case Retrieval by Inductive Approach

Case retrieval through inductive methodology is employed to determine the structural presentation of the case base and chooses the relatively more important and differentiated attribute from the associated cases. Retrieval will acquire the compact brief place for searching in a structured case base.

3.3.3.1.3 Case Retrieval by Knowledge-Guided Approach

Case retrieval by employing the knowledge-guided approach execute its working by make use of specified knowledge of the domain to discover those targeted features of the case that might be fairly significant for the retrieval of cases in future but the importance of the attributes changes as the situation changes .

3.3.3.2 Case Reuse Phase

Once the retrieval successfully retrieves the cases from the knowledge base the second step is to reuse or adaption of the case. In this phase the retrieved case is being reprocessed from the case library and signifies only as the suggested solution for the problem case. Stored cases generally need some modification before recommending as the solution of cur- rent situation. This modification process in stored cases to make them acceptable for the current situation is termed as adaptation. The process of adaptation estimates the vari- ation occurred among the already existing new case and the cases that are already stored .

We are discussing three case adaptation strategies in the following lines [Zia et al., 2014b]:

1 – Reinstantiation strategy: This is the simplest adaptation process in reuse phase of cases. In the reinstantiation strategy we simply adapt the solution of the extracted cases that are most similar to the current situation. There will be no modification and adjustment made in this strategy.

2 – Substitution Strategy: In the substitution strategy, once the retriever has retrieved the most similar case from case library than unsound or unfeasible features that are not interrelated with the current situation will be restore.

3 – Transformation Strategy: In case the appropriate options are not present we than use substitution strategy. Amended solution is being derived in this strategy.

45 3.3.3.3 Case Revise Phase

After completing the process of retrieval and adaptation, cases are now forward to the case revise phase of CBR cycle. This phase is liable to analyze and confirm the accuracy of the adapted case and then presented it as the endorsed solution to the current problem [Begum et al., 2011]. The concept of group clinical decision making process (GCDM) is implemented in this stage where the group of clinicians are advice to the adopted solution of new input case.

3.3.3.4 Case Retain Phase

Case retain phase is the final step in CBR cycle and is liable to integrate new cases into a case base as experienced case for the future use.

The above detailed description of the CBR cycle gives an inside of the primary deter- minants which ensures the efficiency and effectiveness of the system.

3.4 Group Clinical Decision Making - GCDM

The process of decision making occur by means of mutual discussion which held between more than one individual that is a collaborative sharing of knowledge and expertise re- quired in solving a particular situation is termed to be group decision making process. In medical field doctors made group discussions at their routine rounds and morning visits to patients, conferences occurred to solve a specified case and etc [Christensen et al., 1993].

3.4.1 Background

One of the significant research area in CDSS is the group clinical decision making (GCDM) process [Hatcher, 1990] in the period of early 1990s, conduct a research to study the dis- tinctiveness of group CDSS and to make use of analytical hierarchy process(AHP) to reach at clinical inferences in group decision support systems [Hatcher, 1994]. Meanwhile Rao and his associates discovered that however group decision making is wide range research area in medical field but growth of CDSS is still slow due to the limitations occurred in its efficient progressRao et al. [1994]; Rao and Suresh [1995]. Research carried out in 2000 on the grouping and classification of medical decision making process to propose a collaborative clinical decision support system which aid group decisionsRao and Turoff

46 [2000].

A system proposed as MedicalWaretm [Rao and Turoff, 2000] is being incorporate with the architecture of group CDSS. This system is developed with an aim to offer an ac- cessible problem solving support, mining algorithms and methods to access clinical data, proficient inference facility and numerous medical decision support tools along with hy- permedia operation.

The unified group CDSS, Delphi method [Linstone et al., 1975] was utilized to support process of group decision support and to reach on collaborative inferences. Though there are not much available material published in research papers on this area but group clin- ical decision making (GCDM) is now popular in health care industry nowadays [Rangel, 2009].

3.4.2 Group Decision Making Process

The process of group decision making is similar to the real life group consultation in clinical practices. In real life clinical group consultation, there is usually a group facili- tator who helps to invite other consultants to participate in the group consultation and facilitates the whole group discussion process, while the group facilitator should have knowledge about all participated group consultant’s expertise in advance.

The role of a group facilitator is for initializing and facilitating an online group consulta- tion. The group facilitator is liable to provide participated consultants the key information about target case of the patient before the group consultation, so that a participated con- sultant can use the key information that will support for suggesting their views about the target case. The consultants role is for participating in a specific group consultation and providing individual diagnosis preference for target case of the patient.

The concept of group clinical decision making (GCDM) process is implemented in the case revise phase of CBR technique.

47 3.5 Metrics for Evaluating Classifier Performance

It is the measure of evaluating the accuracy range of the classifier’s performance that is used to predict the tuples of class labels. In evaluating phase, we have considered the cases where the class tuples are distributed unevenly or in an unbalanced manner. Table 3.1 summarizes the evaluation measures of the classifier. They include "accuracy (also known as recognition rate), sensitivity (or recall), specificity and precision"[Han et al., 2011]. We have generalized the term accuracy for referring predictive ability of classifier.

Table 3.1: Evaluation Measures [Han et al., 2011]

S. No. Measures Formula TP + TN 1 Accuracy (recognition rate) P + N FP + FN 2 error rate, misclassification rate P + N TP 3 Sensitivity, True Positive Rate, Recall P TN 4 Specificity, True Negative Rate N TP 5 Precision TP + FP

Four additional terms are used for evaluating performance of classifier and we have sum- marized them in Table 3.2. [Han et al., 2011].

• True Positive (TP): shows the positive tuples which were labeled correctly.

• True Negative (TN): shows the negative tuples that which labeled correctly.

• False Positive (FP): shows negative tuples which were incorrectly labeled as positive.

• False Negative (FN): shows positive tuples which were incorrectly labeled as nega- tive.

Confusion matrix is a fundamental tool to analyze how accurate the SVM classifier in recognizing different classes tuples. The attributes of the evaluation measures table is implemented in 6.2 to check the performance level of SVM Classifier.

48 Table 3.2: Confusion Matrix

Prediction outcome yes no Total

True False yes Positive (TP) Negative (FN) P Actual value

False True no Positive (FP) Negative (TN) N

Total P0 N0 P + N

3.6 Chapter Summary

In this chapter we briefly discussed the method and approaches that are used in our proposed KBCDSS. Initially major components of CDSS are explained. we have then explained the importance of constructing well planned KB and its representation for suc- cessful execution of CDSSs. To maintain medical data sets in structured manner we have employed DBMS in our system. We have then briefly explained the employed data min- ing techniques that are case based reasoning and support vector machine. Concept of group clinical decision making to enhance the validity of decisions is also being included in this chapter. Finally we have discussed some evaluation measures that will assist for evaluating the classifier performance.

The next chapter shows the detail description of our proposed online knowledge-based clinical decision support systems (KBCDSS).

49 Chapter 4

Development of the Proposed Tool - KBCDSS

This chapter presents the brief introduction of the proposed tool that is online knowledge- based clinical decision support systems. After that incorporate the steps of Knowledge data discovery (KDD) process map the architecture of the proposed tool along with the methods and techniques used in the proposed tool.

4.1 Introduction of the Proposed System - KBCDSS

The proposed online knowledge-based clinical decision support system(KBCDSS) is a multiple diseases diagnosis system that inaugurates the concept to get together medical consultants of various medical fields on a single platform all the way through web from where they can confirm and authenticate their diagnosis regarding patient diseases eval- uations.

By means of KBCDSS, the clinical experts of diverse medical areas can send and up- load the medical cases that are patient disease attributes beside with their recommended and detailed diagnostic analysis into the system. These piled up cases may possibly be use for the purpose of diagnosis and classification by the medical practitioners all over the world through web. The running of this system will execute as the medical consultant

50 enters the fresh medical case and then make comparison to extract the most similar case that is already being uploaded into the proposed system. This comparison allows medical consultants to extract those cases that are more alike or relating to the current situation. This assessment will provide assistance to clinicians in their decision making during the phase of diagnosis and treatment suggestion. This research is a helping hand for the fresh medical practitioners in their clinical practices while the skilled clinicians can confirm their diagnosis by using the proposed system.

The employment of medical decision support system are less popular due to their mul- tifaceted nature as they are difficult to handle by a non computer based person, certain software experts are required to operate these system which is both the time and cost consuming. This research is accomplished to resolve these challenges. Major challenges faced while developing the CDSS include knowledge acquisition, construction of knowl- edge base, knowledge representation, employment of suitable inference technique and the problem of computer human interaction [Sittig et al., 2008].

Classifying medical knowledge to examine a particular condition is a difficult assign- ment. In medical field huge extent of information is originated which belongs to patient’s documentation, assets of hospitals, medical equipment etc [Holzinger et al., 2014]. These massive consignments of medical information perform significant task for drawing out functional information that assist clinicians during diagnosis and treatment suggestion phase.

Knowledge Data Discovery (KDD) procedure is prominent in identifying effective and knowledgeable information from large repositories of data in different relational databases [Adhikari et al., 2014]. Machine learning, knowledge engineering and management, ar- tificial intelligence, speech and pattern recognition, data visualization, databases, high- performance computing and medical informatics are the core specialization areas of KDD process [Prakash, 2013]. KDD process works together with medical domain to extract out the viable knowledge that assists medical practitioners in their clinical practices shown in figure 4.1 [Milovic, 2012].

The process of KDD primarily concern regarding data collection pattern from different

51 resources, then store these data consignments into the data warehouse. Subsequently the process employs different data mining techniques through effective data retrieving algo- rithms which successfully access the huge amount data, afterward the data representation step engages in the visualization of the extracted patterns and models [Collen, 2012].

Figure 4.1: The Knowledge Data Discovery Processes [Han et al., 2011]

KDD process pursues subsequent steps [Han et al., 2011] which consequently presents prepared data requisite in supplementary proceedings.

i – Procedure of data cleaning: The concerning step is responsible to remove the noise and inconsistent data from the databases and flat files.

ii – Procedure of data integration: This step is liable to combine more than one data sources.

iii – Selection of data: Data selection step involves in the selection of suitable data available in database for the purpose of investigation domain.

iv – Transforming data: This step is for transforming and consolidating data in a suit- able form by means of summary or aggregation operations for mining.

52 v – Data mining: Using different data mining techniques, data is being extracted for the purpose of analyzing the specific situation.

vi – Evaluating the Pattern: This step is used to discover the patterns that represents the knowledge which is based on interesting parameters). vii – Knowledge presentation: This step is responsible in representing knowledge (mined knowledge is being presented to users by using techniques of knowledge represen- tation and visualization).

4.2 KBCDSS Architecture

The proposed architecture pursues the pattern of knowledge data discovery process for mechanized and useful mining of medical domain knowledge that is available in differ- ent relational databases. The construction of this system is a step wise and systematic procedure.

• Initially data is being accumulated from different potential sources of data. This col- lected data is then go through the knowledge acquisition process that consequently updates data from data sources to data warehouse which maintains complete med- ical record that aid clinicians while they are necessitate making use of that record.

• Next, scrutinizing an appropriate knowledge representation scheme for the construc- tion of knowledge base depends on particular domain knowledge. Managing big repositories of medical data is a difficult task and necessitates professional expertise to crack this assignment. In a multiple disease oriented diagnosis system KBCDSS, the knowledge base is being construct by employing structural knowledge represen- tation scheme. So as to accumulate and cope with structural knowledge, database management systems (DBMS) as an appropriate knowledge representation scheme are employed to represent the medical domain knowledge and pile up that knowledge in the relational database system. Database management systems (DBMS) deemed to be excellent knowledge representation scheme but its flaw is their weakness and incompetency in making inferences and concluding results.

• Subsequently implements hybrid reasoning approach that is an integration of case base reasoning and support vector machine as an inference mechanism. Case based

53 reasoning technique is famous in acquiring knowledge and making inferences by making use of previously accumulated cases.

• Afterward the proposed system provides a user friendly GUI environment to its clients so that the problem of human computer interaction is being resolved effi- ciently.

Figure 4.2: A Proposed Framework of Knowledge-based Clinical Decision Support System (KBCDSS) [Zia et al., 2015a]

The architecture of the proposed KBCDSS shown in figure 4.2 is primarily comprises of five Layers namely layer 1 that works as a potential source of knowledge or medical cases data gathering, layer 2 is knowledge acquisition layer, layer 3 is data warehouse server layer, layer 4 is knowledge base and inference engine layer and finally layer 5 is the graphical user interface environment to facilitate the interaction between the medical

54 practitioners and system.

The proposed KBCDSS framework and the prototype application based on the following Health-care Standards and their details are given in appendix E.

• Health Level - 7

• Clinical Data Interchange Standards Consortium

• Integrating the Health-care Enterprise

Below we have shown the details of the health-care standards that are mapped on the proposed framework of KBCDSS.

I– The layer 1 is based on HL7 Version 2.x and 3 messaging standard framework used for Interoperability specification for health and medical transactions.

II– The layer 2 is based on Clinical Document Architecture (CDA) of HL7 standard used for clinical documents based on HL7 version 3.

III– The layer 3 is based on Continuity of Care Document (CCD) of HL7 used for exchange of medical summaries based on CDA.

IV – The layer 4 is based on Clinical Data Interchange Standards Consortium (CDISC) used for analysis and reporting of results.

V– The layer 5 is based on Clinical Context Object Workgroup (CCOW) used for interoperability specification for the visual integration of user applications.

VI – The prototype application of online KBCDSS is based on Integrating the Healthcare Enterprise that includes HL7 and CDISC.

For extracting out knowledge, the proposed system incorporate with KDD process so that the resultant knowledge will facilitate medical practitioners to take their decisions.

4.2.1 Potential sources of knowledge:

The layer 1 is reveals as the potential foundation of knowledge which is responsible in providing medical data to the clinicians. Potential resources of knowledge consist of cases that are solved and saved as experienced cases, material from the reference books, mul- timedia files, flat files, databases (public and private), investigated or researched reports and informative stuff offered on different websites.

55 4.2.2 Knowledge Acquisition Process:

The layer 2 demonstrates the knowledge acquisition process. “Knowledge acquisition is the gathering, transfer and conversion of trouble-cracking skills from expert profession- als or other knowledge providing resources to a computer program for assembling and expanding the knowledge base” [Turban et al., 2005]. The proposed online KBCDSS fo- cuses on using the information restored from the previous cases rather than incorporating domain knowledge of disease. The medical practitioners of the specific disease upload their medical cases in the form of *.xls or *.csv file format to online KBCDSS used for acquiring the knowledge from the medical cases that will help for decision making process rather than decision based on domain knowledge.

The knowledge acquisition process follows the following steps:

1. Extracting Data: Medical experts take out the required sets of medical data from the possible resources of knowledge which consists of data provides in reference books, multimedia credentials, flat files, databases (public and private), research reports, information from medical experts and internet information.

2. Cleaning Data: After data extraction, the data cleaning process starts which are accountable to remove noise and in-consistence data from the databases and flat files.

3. Data transformation: once the data cleaning process has done then the filtered data is being transformed into data warehouse server.

4. Loading: This step is responsible to upload the data into database.

5. Refresh: Updates being extended to data warehouse from data sources.

4.2.2.1 Algorithm for Knowledge Acquisition Process in KBCDSS

We have proposed knowledge acquisition algorithm in our proposed KBCDSS to perform pre-processing steps in acquiring domain specific knowledge.

4.2.3 Data Warehouse Server Layer:

Layer 3 shows the data warehouse server which is a part of relational database manage- ment system. In the proposed model of KBCDSS, layer 2 performs knowledge acquisition

56 Algorithm 1 Knowledge Acquisition Process Require: Medical Cases Ensure: Refined knowledge.

1: Upload Medical Cases in the form of *.csv or *.xls file format.

2: Save selected file on server.

3: Open *.csv file.

4: Initialize header[] //Initialize Array Variable

5: header[] ← Header of each column of the file.

6: Initialize Abbreviation //Initialize Array Variable

7: i ← 0;

8: while i ≤ header.length do

9: Initialize found = false;

10: Initialize temp ← header.[i] // Only Select Starting Four Character for Aliases of the Header.

11: Initialize j ← 0;

12: while j ≤ header.length do

13: if T emp = header[j].toString(4) then

14: found = true;

15: end if

16: if found = false then

17: Abbreviation = temp

18: else

19: Abbreviation = temp + i;

20: end if

21: end while

22: end while

23: Save Header to Database

24: data[]; // Initialize array variable to read each line of *.CSV File.

25: Initialize i ← 0;

process and as a consequence data is updated from the data sources to the data warehouse. The warehouse database server retains all the different medical records that are stored in a relational table. The data warehouse server also maintain meta-data repository that

57 26: while not data.eof() do

27: drColumn[] = data[i].split(”, ”);

28: while i ≤ data.length do

29: if data[i] == empty or data[i].contain(?) then

30: Delete data[i];

31: else

32: str = str + data[i]+",";

33: end if

34: end while

35: Save str to database

36: end while

37: Display Refine Knowledge as an output

retains all the information or records which relates to data warehouse.

4.2.4 Knowledge-base and Inference Mechanism

Layer 4 consists of knowledge base and inference mechanism.

4.2.4.1 Knowledge Base Construction and Its Representation

Knowledge base can be explained as a systematized compilation of specific field knowledge which is involved in resolving problems arise during decision making phase. To execute the efficient implementation of CDSS there is a necessitate making well constructed and planned knowledge base. The knowledge base comprises of real facts, imperatives, heuris- tics, and other related data, and is being utilized by the inference mechanism to present proficient judgments and other functional resources for the users through an interface.

Scrutinizing suitable knowledge representation scheme for constructing a knowledge base is dependent on specific domain knowledge. Organizing huge repositories of medical knowledge is a complicated assignment and it involves proficient skills to sort out this task. There is a need to employ structural knowledge representation scheme in the proposed model of an online KBCDSS. In an attempt to accumulate and deal with structural knowl- edge, we are employing database management systems (DBMS) as a suitable knowledge representation scheme which characterize the medical domain knowledge and load that

58 knowledge in the relational database system. Database management systems (DBMS) are supposed to be an outstanding knowledge representation scheme but incapable and incompetent in providing inferences and conclusions [Kong et al., 2008].

4.2.4.1.1 Algorithm for KB Construction and its Representation

In our proposed KBCDSS construction of KB in a planned way and its structural rep- resentation has been done through developing an algorithm for the KB construction and its representation.

Algorithm 2 – Knowledge Base Construction & Its Representation Require: Read header data. Ensure: Store Knowledge in a Knowledge-base and Represent Knowledge.

1: Read Header table.

2: Initialize i ← 0; //Initialize Variable

3: while i ≤ header.length do

4: print header[i].value

5: println;

6: Assign predictive & response value in a header table

7: Save header data in a Knowledge-base.

8: end while

9: print "Case Solution"

10: Initialize i ← 0;

11: while i < header.length do

12: if header[i] = ”ResponseV alue” then

13: print header[i].value

14: end if

15: end while

4.2.4.2 Inference Mechanism

Once the assignment of constructing knowledge base and its representation is being com- pleted then there is need to employ suitable mining techniques by using efficient retrieval algorithms that effectively access the large extent of data for making inferences. An infer- ence mechanism employed in a CDSS is very much related to the corresponding knowledge

59 16: print "Case Description"

17: Initialize i ← 0;

18: while i < header.length do

19: if header[i] = ”predictiveV alue” then

20: print header[i].value

21: end if

22: end while representation scheme used in that system. [Turban et al., 2005].

4.2.4.2.1 Hybrid Reasoning Approach

The proposed framework of online KBCDSS uses hybrid reasoning approach of case base reasoning and support vector machine to handle the troubles of high complications and uncertainty in medical conditions. The integrated approach of CBR and SVM is employed to solve the problem if one of the techniques fails to take out the required solution. Hybrid reasoning models carry out equally the computation and analysis procedure in diagnosis and are supposed to be very efficient but are less popular to put into practice [Pandey and Mishra, 2009]. Clinical Decision Support System (CDSS) uses hybrid approach that help the medical practitioners to tackle problems of high complexity, and changing medical conditions [Ting et al., 2011; Saha and Kumar, 2013].

For extract out the suitable information from the Knowledge base, we therefore used case based reasoning and support vector machine as a hybrid reasoning approach in our model. The detail description of the hybrid reasoning technique is discuss in chapter 5.

4.2.5 Human Computer Interaction Layer

Layer 5 shows the graphical user interface environment of the KBCDSS. Using the GUI environment the medical practitioners input the medical cases of the patient in to the system. Then the propose system generates the results that are based on input and resultant data is shown in text form as well as in graphic form to the medical consultants. The medical practitioners treat that resultant data as knowledge of the current input problem and that will help for taking their diagnosis.

60 4.3 Chapter Summary

This chapter describes the detailed explanation of our proposed online knowledge-based clinical decision support systems (KBCDSS). we have then identified the importance of domain knowledge in medical field to make case-specific decisions. Then we discussed the significance of knowledge data discovery (KDD) process in identifying viable knowledge which is prerequisite in making decision accurately. After that we have explained in detail the architecture of our proposed system which peruses the pattern of KDD process for mechanized and useful mining of knowledge. Along with this we have given the details of our proposed algorithms that are knowledge acquisition algorithm and the algorithm for constructing KB and its representation. Concept of hybrid reasoning approach that are CBR and SVM to carry out inference mechanism are also discussed.

In the next chapter, we briefly discuss the employed reasoning techniques in KBCDSS.

61 Chapter 5

Proposed Reasoning Techniques

This chapter describes the brief introduction of data mining and identifying the importance of data mining that are helpful for knowledge extraction along with the some important data mining algorithms. After that discuss the two main reasoning data mining techniques that is case based reasoning and support vector machine. These reasoning techniques are used as a hybrid reasoning techniques used to extract out the medical knowledge from the knowledge base. Then discuss the proposed Hybrid architecture where we implement these two techniques. After that discuss the proposed hybrid CBR architecture along with the system flow chart that shows the functioning of the proposed system.

5.1 Introduction to Data Mining Techniques

Since last two decades, number of data mining techniques are considered to be functional or useful for the diagnosis purpose [Begum et al., 2011]. Data mining techniques bring together the data analysis methods with different algorithms to process large and complex sets of data [Kantardzic, 2011]. For solving the DM tasks, different algorithms and tech- nique are employed namely: Support vector machine, neural network, fuzzy logic, rough set theory, association rule mining, decision tree, genetic algorithms and case based rea- soning [Salem, 2011]. Case based reasoning (CBR) and Support vector machine (SVM) are used as an inference mechanism in our proposed online Knowledge-based clinical de- cision support system (KBCDSS).

62 5.2 Case Based Reasoning Technique

Case based reasoning approach is being widely used during crucial phases of patient care which include diagnosis and treatment suggestion. "CBR is an appropriate method to explore in a medical context where symptoms represent the problem, and diagnosis and treatment represent the solution" [Marling et al., 2014].

The fundamental feature of the CBR is an inter-thread procedure of four phases in- troduced by Aamodt and Plaza which can be explained by a schematic cycle namely retrieval, reuse, revise and retain shown in figure 5.1 [Aamodt and Plaza, 1994; Zia et al., 2015b].

Figure 5.1: Schematic Cycle of CBR Technique [Zia et al., 2015b]

• Retrieval phase is an initial step which inquires about previous experiences which are identical to the current situation. In this phase alike cases will be extracted from the case repository on the basis similarity with new input case.

• Reuse phase is the second step which is responsible in suggesting a solution for the new case from the available solutions of the cases that were retrieved from the case repository.

• Revise Phase: Solution proposed into reuse phase will be then revised by an expert (either human or machine).

63 • Retain Phase: Once the solution is revised by an expert it has to be determined whether to keep this new solution in case repository in order to facilitate future diagnosis of new case. Keeping proposed solution in case repository as a new case is retain phase of this cycle.

5.2.1 CBR Technique as an Inference Engine in Online KBCDSS

Case based reasoning is considered to be an ideal technique to obtain knowledge and make inferences by making use of previously stored cases. In case based reasoning sys- tem, knowledge is personified as historical cases. These cases are consisting of detailed description of problem along with their way out. For solving new case, we compare previ- ously stored cases with new problem case and then repossess the similar cases. In medical domain we employ CBR technique for diagnosis purpose as it compares attributes of current case with the stored cases in knowledge base.

5.2.2 Case Retrieval Phase of CBR technique

Retrieval phase is deemed to be fundamental part of CBR technique. In retrieving phase an efficient way to extract out alike cases is being used. The case retrieval process identifies to retrieved most suitable cases present in case repository that are closely related to new given case. Retrieval phase is further divided into four sub tasks [Aamodt and Plaza, 1994; Zia et al., 2014b]:

i Features Recognition: In order to accomplish an efficient retrieving process there is a need of setting some standards or criterion for selection of cases. This would de- termine which case is considered to be most suitable for retrieval purpose. Medical practitioners or clinicians are responsible for the accomplishment of an efficient re- trieval of cases. They input features values and on the basis of these features values system retrieves those cases that are related to new input case values.

ii Case Inspection: Retrieval phase of CBR system depends on memory representation and indexing method of previously stored cases into case repository.

iii Similarity Measurement: Cases that are to be retrieved from case repository were initially matched with new case on the basis of similarity in feature values.

iv Retrieve Similar Cases: Once the similarity computation has done, most similar cases extracted from the case repository that are closely matched with new given case.

64 5.2.2.1 Processing of Case Retrieval Phase

The foremost step of CBR cycle is case retrieval phase. In this phase retriever extracts out most similar cases for solving current situation. Retriever input new case and compare it with stored cases in case repository. Similarity measurement is the basis for making comparison among cases. The process of case retrieval phase of CBR technique is shown in figure 5.2. Similarity measurement will allows retrieving one or more cases from case

Figure 5.2: Case Retrieval Phase of CBR Cycle [Zia et al., 2015b] repository. Once the cases are being extracted out from case repository, retriever will make some scrutiny to check whether retrieved case is similar enough to new input case or there is necessitate making some modifications into the searching parameters to extract again. The case retrieved during this analysis will forward to case adaptation phase. On the contrary if there is no further appropriate retrieval then concluded as failure of the retrieval process.

In our proposed model of KBCDSS medical experts primarily recognize particular disease (i.e. identify the disease type). Then assign weights to attributes of that diseases on the basis of their importance. These attributes are signs and symptoms of patients. After- ward case retriever input new case in case repository and compare its attributes with the cases that were stored earlier. To solve the current case, alike cases are retrieved from case

65 repository. For the accomplishment of an effective retrieval process there is necessitate making some selection criterion for retrieving cases. This selection criterion will identify which case is closer and appropriate to retrieve. The degree of closeness of cases will be determined by using similarity metric. Ranges of similarity value lies into 0 to 1, where 0 show cases are not matched and 1 shows that cases are perfectly matched [Zia et al., 2014b].

5.2.2.2 Calculate distance among the new case and the old cases

Different algorithms are applied to check similarity among new input case and stored cases in a case repository. Average weighted Euclidean distance is used in our proposed model for calculating distance between new input case and previously stored cases. For easy computation, the range of the distance measured between the new and stored cases can be normalized into 0 to 1. For this purpose, CMax is used for converting the value into normalized form. The weighted Euclidean distance formula for calculating distance between input case and pile up cases which were kept in case repository shown in eq. 5.1 [Ahmed, 2010; Zia et al., 2014b] .

r 2 Pn CNi−COi Wi i=1 CMaxi d(CN ,CO) = Pn , (5.1) i=1 Wi

Where

– CN = new referred case from clinicians

– CO = Previously stored case in a case repository.

– CMax = Maximum value selected from the new referred case or previously stored case for converting it in normalized form.

– n = Attributes in each case.

– i = is an individual or signal attribute.

– W = Weight of each attribute. These weights determine the importance of each at- tributes and are assigned by field experts.

5.2.2.3 Compute similarity between new case and old cases

Once distance among new input case and stored cases is computed in normalized form then apply similarity measurement function that provide most suitable case match with

66 the current situation. The calculation performs for computing similarity measurement function shows in eq. 5.2 and eq. 5.3, respectively [Behbahani et al., 2012; Zia et al., 2014b].  

Sim(CN ,CO) = 1 − d(CN ,CO) ∗ 100, (5.2)

r 2  Pn CNi−COi  Wi i=1 CMaxi Sim(CN ,CO) = 1 − Pn  ∗ 100, (5.3) i=1 Wi

5.2.2.4 Proposed Retrieval Algorithm

Subsequently we have shown the processing of our proposed retrieval algorithm for re- trieving similar cases.

Algorithm 3 – Retrieve Similar Records from the Case Repository Require: New Case value. Ensure: Similar Record Retrieved.

1: Select Category.

2: Select Sub-Category.

3: Input Threshold value.

4: Initialize i ← 0; //Initialize Variables

5: while i ≤ header.length do

6: print header[i].value

7: print drop down list for weight (from 1 to 10)

8: input box for new case value

9: end while

10: Assign weight for each attribute by medical experts

11: Input New Case Value of the Patient

12: Initialize i ← 0; //Initialize Variables

13: while i ≤ MedicalDataSet.table[0].Rows.count do

14: Initialize T otalW eight ← 0;

15: Initialize j ← 0;

16: while j ≤ ds.table[].rows.count do

17: Initialize row = ds.table[j].row;

18: Initialize ColNameV alue = row[DBF ieldName];

67 19: Initialize W eight = row[”W eight”];

20: TotalWeight = T otalW eight + W eight;

21: if ColNameV alue < UserInputV alue then

22: d = d + (((1 − P ower(Sqrt(Abs(UIpV al − CNameV al)/UIpV al)), 2))) ∗ W eight);

23: Else

24: d = d + (((1 − P ower(Sqrt(Abs(CNameV al − UIpV al)/CNameV al)), 2))) ∗ W eight);

25: end if

26: end while

27: row[”GlobalSimilarity”] = Math.Round(((d/T otalW eight) ∗ 100), 3); 28: end while

29: SortMedicalDataSettabletoGlobalSimilarityDesAssending;

30: printF ilterrecordGlobalSimilarity >= T hresholdvalue; 31: Retrieve similar record

5.2.3 Case Reuse Phase

The second step of CBR cycle is case reuse or case adoption phase. In this step one of the retrieved case is being reused and then proposed as a solution of current case. There are some modifications required in reusing past solutions. Case reuse phase of CBR cycle is shown in figure 5.3.

Figure 5.3: Case Reuse Phase in CBR Cycle [Zia et al., 2015b]

68 Case adoption is a procedure of fixing previous solution. An adoption of obtained so- lution is needed to generate accurate results. This adoption process could be done via domain experts who have determines if the suggested solution is reasonable or need some modification and it can also be processed by applying different rules or algorithms for calculating differences among extracted cases and current case.

5.2.3.1 Case Adaption Strategies of Case Reuse Phase

Subsequently three case adaptation strategies are discussed: [Pal and Shiu, 2004; Zia et al., 2014a]

i Reinstantiation: This is a straightforward way of adaptation. In this strategy, simply copied the solution of case that is most similar to present situation and used it for the current problem without any modification or adjustment.

ii Substitution: This strategy restores elements of previous solution that are unsound with the new input case.

iii Transformation: In the absence of suitable alternatives, use transformation strategy. In this situation a modified solution is derived.

In our proposed KBCDSS, we employ reinstantiation strategy for adopting solution of most similar case and mapped this solution to new case. In case similar cases are not found, the technique of SVM will be employed to predict the solution of the new case.

5.2.4 Process of Case Revise Phase

Once the case is retrieved and adapted, it is then proceed to revise phase. In this phase, adapted solution is being analyzed and verified for accuracy and offered as a validated solution of new problem case [Pal and Shiu, 2004; Zia et al., 2014a].

The concept of group clinical decision making (GCDM) is implemented in this stage. In GCDM, medical practitioners invite group consultants to participate in group con- sultation. The group consultants give their advice or comments about adapted solution through proposed system. The medical practitioners check comments of adapted solution for new case given by group consultants and if found three positive comments for sug- gested solution then confirm that solution for new case and resultant case has forwarded to the case retain phase of CBR technique.

69 5.2.5 Process of Case Retain Phase

This is the final or last stage of CBR cycle and it is responsible for integrating new cases into the case repository or knowledge base for future reference [Pal and Shiu, 2004; Zia et al., 2014a].

5.3 Support Vector Machine (SVM)

Support vector machine has become a new approach in machine learning techniques that are based on statistical learning theory and this technique has achieved various updated inferences in accurate classification [Alpaydin, 2014].

The SVM technique executes its working by mapping the actual input space into the elevated dimensional dot product space which is called the featured space. Within a fea- ture space the maximum hyperplane is obtained to optimize the classifier’s simplification capability. The maximum hyperplane is obtained by utilizing the optimization theory, and the relevant insights are supplied by the statistical learning theory [Han et al., 2011]. The figure 5.4 shows more clearly the idea of an optimal hyperplane for the patterns that are linearly separable.

Figure 5.4: A Linear Separable SVM [Han et al., 2011]

70 5.3.1 Linearly Separable Medical Data

In medical cases, data is stored along with their feature values and the decision has taken by the medical practitioners. In most of the medical data that are linearly separable based on their medical decision. Consider a Wisconsin breast cancer data set from UCI Machine Learning Repository where two-class problem that are the linearly separable classes. Table 5.1 shows the predictive values and Response values of the breast cancer medical data set. Table 5.1: Predictive and Responsive Values of the Breast Cancer Data Set

Attribute Type List of Attributes Values Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Predictive Marginal Adhesion 1 - 10 Values Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Responsive Class Decision 2 for Benign, 4 Value for Malignant

Suppose that we have the data set D as:

(X1, y1), (X2, y2), ..., (X|D|, y|D|) Where

• Xi = Training tuples or feature values that are correlated with yi and

• yi = Class labels, where yi can take one of two values either the +1 or -1, i.e.(yi ∈ {+1, −1}) corresponding to the classes, that is class-decision = benign and class- decision = malignant, respectively.

Graphically we have seen that a straight line drawn is separating all the tuples of class +1 from the tuples of the class -1 which actually explains that the data is linearly separable that is shown in figure 5.5. An infinite number of lines could be drawn here for separating

71 the tuples. Analysis conducted is to find the “one” which will generate minimum possible error of classification on the tuples that are previously unseen.

Figure 5.5: The Training Data are Linearly Separable [Han et al., 2011]

Data is being generalized to the “n” dimensions and we have to find which one is the best hyperplane. We are using the hyperplane refer to as the boundry of decisions to be taken and this is independent of the number of the attributes either it is based on two or three or more than three attributes. The best hyperplane is selected using the SVM approach which used to search the maximum marginal hyperplane (MMH). Margin can be define as the shortest possible distance to a hyperlane ranging from one side of its margin equals to the shortest possible distance to the hyperplane ranging from the other side of its margin and these sides of the margin are equally parallel to the hyperplane.

Figure 5.6 which depicts two possible separating hyperplane and the margins that are correlated with their respective hyperplanes. The both hyperplanes drawn are perfectly classifying all given sets of data tuples. When it comes to classify the future data tuples the hyperplane with larger margin are intuitively more accurate as compare to the hy- perplane with smaller margin. Maximum margin hyperplane is being searched during the training phase by support vector machine as it provides the maximum possible separation in between classes.

72 (a) Small Margin (b) Large Margin

Figure 5.6: Two Possible Separating Hyperplanes and their Associated Margins [Han et al., 2011]

Mathematically the separating hyperplane is given in eq. 5.4,[Han et al., 2011]

W · X + b = 0 (5.4) where

• W is a weight vector, that is W = w1, w2, ..., wn.

• n is the number of attributes.

• b is a scalar (bias).

lets take an example of two input attributes i.e. A1 and A2 as shown in figure 5.6(b).

Training tuples are 2-D (e.g., X = (x1, x2)), where x1 and x2 are the values of attributes

A1 and A2, respectively, for X. In case we have ‘b’ as an additional weight, W0 then, eq. 5.3.1 can be rewrite and shown in eq. 5.5 as: [Han et al., 2011]

w0 + w1x1 + w2x2 = 0 (5.5)

Condition that is satisfied if any of the point lies above the separating hyperplane given as in eq. 5.6,[Han et al., 2011]

w0 + w1x1 + w2x2 > 0 (5.6)

73 In the same way condition if any point lies below the separating hyperplane given as in eq. 5.7, [Han et al., 2011]

w0 + w1x1 + w2x2 < 0 (5.7)

The weights can be adjusted so that the hyperplanes defining the “sides” of the margin can be written as,

H1 : w0 + w1x1 + w2x2 ≥ 1 for yi = +1 (5.8)

H2 : w0 + w1x1 + w2x2 ≤ 1 for yi = −1 (5.9)

This means that tuples falls on or above the H1 are related to class +1 shown in eq. 5.8,

Or tuples fall on or below H2 are related to class -1 shown in eq. 5.9. Integrating eq. 5.8 and eq. 5.9, we have,

yi(w0 + w1x1 + w2x2) ≥ 1, ∀i (5.10)

Support vectors are the training tuples that are falling on the hyperplanes H1 or H2 which are actually the sides of margin and this condition is satisfying the eq. 5.10. This situation also indicates that these are equally closer to the separating maximum marginal hyperplane (MMH).

Figure 5.7: Support Vectors are Shown with a Thicker Border [Han et al., 2011]

Consider figure 5.7, the support vectors are surrounded by a thicker border. Classification

74 of support vector is very difficult task and it gives the most accurate results related clas- sification. Above analysis provides the formula measuring the size of maximum margin.

From any point on H1 the distance from the separating hyperplane is given as in eq. 5.11.

1 (5.11) ||W || √ Where, ||W || is called Euclidean norm of W, that is W.W . If W = w1, w2, . . . , wn then √ q 2 2 2 W.W = w1 + w2 + ··· + wn.

According to the definition this is exactly equivalent to the distance measured from sep- arating hyperplane to the any point lies on H2. Hence we have the formula that is shown in eq. 5.12 for maximum margin equals to,

2 (5.12) ||W ||

We can simply employ any optimization software package to solve the restrained convex quadratic troubles and to find the maximum margin and support vectors. After the ac- cumulation of support vectors the trained support vector are available which can be used the classification of linearly separating data. These are referred as the linear support vector machine.

According to the Lagrangian formulation mentioned, decision boundary of maximum marginal hyperplane (MMH)can be written as in eq. 5.13,[Han et al., 2011]

l T X T d(X ) = yiαiXiX + b0 (5.13) i=1 where,

• yi = Class labels corresponding to the support vectors Xi.

• XT = Test tuple.

• α and b0 = Numeric parameter (can be obtained automatically by svm algorithm and it is to be noted that the alpha is the Lagrangian multiplier).

• l = number of support vectors

75 When the data is linearly separable the support vectors are considered as the subsets of the actual training tuples.

In our proposed KBCDSS, given a test tuple, XT in eq. 5.13, we have checked the sign of the result to identify data lies above or below the MMH. This will inform that test tuple falls over which side of the hyperplane. In case there is a positive sign, then XT falls on or above the MMH, and SVM predicts that XT relates to class +1 (showing cancer-decision = benign). In case there is negative sign, then XT falls on or below the MMH and SVM predict that XT relates to class -1 (showing cancer-decision = malignant).

5.4 Proposed Hybrid CBR Cycle

In order to enhance the level of accuracy in decision making process we have proposed a new hybrid CBR cycle in KBCDSS which employs CBR technique as a core methodology and SVM as a sub technique to process the inference mechanism. The hybrid CBR cycle is shown in figure 5.8.

Figure 5.8: A New Hybrid CBR Cycle

To process the new input case we employ our proposed new hybrid CBR cycle as an inference mechanism in KBCDSS. To make inferences about new input case we primarily

76 undergoes the case retrieval phase in which most similar cases are obtained from case library or knowledge base. Once the retrieval of cases is being successfully accomplished then reinstantiation strategy of case reuse phase is used to adopted the suggested solution for new input case.

In case retrieval of similar case is not being successfully accomplished or there is no similar case found we then employ support vector machine as the sub technique to con- tinues the inference mechanism. SVM is used to predict the solution of the new input case by means of available training tuples. This suggested solution is then forwarded to case revise phase. In case revise phase, we have included the concept of group clinical decision making process (GCDM). The process of GCDM is responsible to conduct group consultation for validating the predicted or suggested solution. Finally, this validated so- lution is forwarded to case retain phase and kept as experiences case for future reference. System flow diagram is shown in figure 5.9.

Figure 5.9: The System Flow Diagram of Hybrid CBR Diagnosis Process

77 5.5 Chapter Summary

In this chapter, we briefly discussed the employed reasoning techniques in our proposed system. To improve the efficiency of inference mechanism, we proposed new hybrid CBR cycle which used CBR as a core methodology and SVM as sub technique. In case retrieval phase of CBR technique, the proposed similarity algorithm for efficient retrieval of similar cases. If the case retrieval phase is successful then reinstantiation strategy for adopting the most suitable solution is used in case reuse phase of CBR technique. Then in case revise phase of CBR technique we have included the concept of group clinical decision making to improve the accuracy of decisions and then retain that phase in case revise phase of CBR technique. If the retrieval of cases is not successful we then used SVM to predict results and make conclusions.

The next chapter shows the implementation of the proposed system KBCDSS on a breast cancer medical data set.

78 Chapter 6

Implementation of the Proposed System - KBCDSS

This chapter presents the experimental analysis of the proposed online knowledge based clinical decision support systems (KBCDSS) where we used breast cancer medical data set from UCI machine learning repository and generates the result. Also shows the measure for assessing the performance level of SVM classifier to predict class decision of breast cancer medical data set.

Online KBCDSS is deployed as an effective prototype application in the medical field. Medical practitioners can use this application during patient evaluation phase it’s because this system has the competency to provide detailed structured knowledge to its users. In order to accomplish an effective functioning of this system certain course of action is followed for data analysis on the Wisconsin breast cancer data set from UCI Machine Learning Repository and implements this medical data set on proposed KBCDSS tool.

6.1 Breast Cancer Medical Data Set

Breast cancer is classified into two classes i.e. benign (considered as non-cancerous tissue) and malignant (considered as cancerous tissue). The conducted study aimed to diagnose if the tested breast cancer condition comes under the class i.e. benign or malignant [Chen et al., 2011; Aruna et al., 2012; Lotfy Abdrabou and Salem, 2010]. To build up the case

79 library we have used Wisconsin breast cancer data set that were obtained from University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. Samples inside the data set arrive periodically as Dr. Wolberg reports his clinical cases. The number of instances inside the data set is 699 (as of 15 July 1992) [Lotfy Abdrabou and Salem, 2010; Zia et al., 2014b]. These records are containing ten attributes along with the class decision attribute.

Table 6.1 the number of attributes and their probable values are presented. Elements relates to benign class are 65.5 % whereas 34.5 % elements belongs to malignant class. 54 elements were excluded from case library as they are incomplete or some attributes are missing in it [Salem, 2013; Zia et al., 2014b].

Table 6.1: Wisconsin Breast Cancer Data Set Attributes

Possible Min. Max. # Attribute Name Mean Median Mode SD Variance Value Value Value

1 (Case ID) Id Number 61634 13454352 −− −− −− −− −−

Clump 2 1 - 10 1 10 4.42 4 1 2.82 7.94 Thickness

Uniformity of 3 1 - 10 1 10 3.13 1 1 3.05 9.31 Cell Size

Uniformity of 4 1 - 10 1 10 3.21 1 1 2.97 8.83 Cell Shape

Marginal 5 1 - 10 1 10 2.81 1 1 2.86 8.15 Adhesion

Single 6 Epithelial Cell 1 - 10 1 10 3.22 2 2 2.21 4.91 Size

7 Bare Nuclei 1 - 10 1 10 3.54 1 1 3.64 13.28

Bland 8 1 - 10 1 10 3.44 3 2 2.44 5.95 Chromatin

9 Normal Nuclei 1 - 10 1 10 2.87 1 1 3.05 9.32

10 Mitoses 1 - 10 1 10 1.59 1 1 1.72 2.94

(2 for 11 Class Decision Benign, 4 for 2 4 2.69 2 2 0.95 0.9 Malignant)

80 6.2 Experimental Analysis and Results

The medical practitioners used the proposed system for classification and diagnosis the disease of the patient. They first perform the knowledge acquisition process in which gather the medical data from different potential sources like own medical cases of their patients, different medical data web sites, research journals and hospitals.

Figure 6.1: Knowledge Acquisition Process in KBCDSS

Figure 6.1 shows the knowledge acquisition process in the processed online KBCDSS. In this process medical practitioners identify the main diseases like Cancer and also men- tioned the sub category like Breast Cancer then medical practitioners upload the breast cancer medical data set that is in (*.csv) format. Accuracy in storing cases in the case repository or knowledge base is essential to make accurate decisions during diagnosis and treatment phases of patient care. Once the medical cases are uploaded then the proposed system initially cleans all the inappropriate data from the current uploaded file.

Figure 6.2: Cleaning the Garbage Data

81 Figure 6.2 shows the cleaning process from the uploaded breast cancer data set. This cleaning process has minimized the chances to make less accurate and incorrect decisions. After cleaning the garbage data from uploaded file, medical practitioners now focus onto the formation of knowledge base that is shown in figure 6.3. This is being carried out by identifying attributes used for case description and case solution that will be used for the construction of case base or knowledge base for specific disease.

Figure 6.3: Construction of Knowledge Base

The case description attributes are those attributes that retains values of the pathological report and case solution attributes kept decision of that particular pathological report. After assigning case description and case solution attributes by medical practitioners, the proposed system utilize these attributes to construct case definition of a particular dis- ease.

Database management system is used to store relevant information of a specific disease in a knowledge base that will be treated as knowledge and also act as a representation scheme of the stored knowledge for the specific disease. Figure 6.4 basically shows case representation of the breast cancer data set that comprises of case description attributes that are used for predictive values of data and case solution attributes that are used for responsive value of selected medical data set. These attributes are stored in case repos-

82 Figure 6.4: Case Representation of a Breast Cancer Disease itory or knowledge base. There are multiple cases of different diseases that are stored in a data base and it will help the different field of medical experts for identifying the important attributes of the disease that will be beneficial to their decision making pro- cess. While building the case repository or the knowledge base of the specific disease, the

Figure 6.5: Diagnosis Screen of the Proposed System – KBCDSS

83 medical practitioners will go for the diagnosis phase. In this phase, medical practitioners select main category of the disease and then select the sub category of the disease along with the threshold value. This will help medical experts for retrieving the similar records that should not be below the identified threshold value. The proposed system set the percentage of the threshold value is 85%, so that the system will retrieve cases that will be greater than or equal to the identified threshold value. Figure 6.5 shows diagnosis screen of the proposed system that will help the medical experts to retrieve the similar cases based on new input case.

After that medical practitioners assigned the weights to each attributes of disease that starts from [1 to 10] i.e. 1 is minimum and 10 is the maximum weight of the feature value along with the pathological report value of the patient. These weights determine the importance of each attributes and will be different from the opinion of the different medical experts. The medical experts input the pathological report value to the proposed system and that will be treated as a new case. on the basis of the input values and the weights assigned for each attributes, the proposed system retrieves the most similar cases that will help the medical experts for their diagnosis process. To retrieve the most similar cases, the proposed system will used average weighted Euclidean distance method shown in eq. 5.1 for calculating the distance between the new case and the stored cases from the case repository. Then apply the similarity measurement function shown in eq. 5.2 and eq. 5.3 that will retrieve the most similar cases from the case library on the basis of the new input case. Figure 6.6 shows the most similar cases that are retrieved from the case repository on the basis of the new input case values.

Figure 6.6: Retrieve the Most Similar Cases from the Case Repository (Textual Mode)

84 Figure 6.7: Retrieve the Most Similar Cases from the Case Repository (Graphical Mode)

Figure 6.7 shows the graphical representation of the retrieved similar cases from the case repository. With the help of similar cases that are retrieved from the case repository, the medical practitioners can easily evaluate the highest similar case that correlates with the new input case values. After retrieving the most similar cases from the case repository on the basis of the new input case then the medical practitioner will now go to adopt the solution of the most similar case that will be the solution of the new input case. The pro- posed KBCDSS tool helps the medical experts to adopt the solution of the most similar case with respect to the new input case. The medical experts adopt the solution of the most similar retrieved case that is the solution of the new input case.

Figure 6.8: Adopt the Solution of the Most Similar Case that Retrieved from the Case Repository

Figure 6.8 shows the case adoption process in the proposed KBCDSS tool. The process of

85 the case adoption is to show the new input case along with their weights of each attribute and the most similar retrieved case along with the solution and their similarity percent- age. The medical practitioners check the similarity percentage of the retrieved cases and then adopt the solution of the most similar retrieved case as the suggested solution of the new input case. If the similar cases are not found from the case repository then support vector machine will predict the solution of the new case. After completion of the adoption process, the medical practitioners forward the new case along with the suggested solution to the group consultants of the same medical domain for the confirmation of the suggested solution for the new input case.

Figure 6.9: New Case with Suggested Solution

Figure 6.9 shows the case revision process of the new input case that send to the medical experts for their views regarding the suggested solution of that case. The concept of group clinical decision making (GCDM) is implemented in case revision phase. The participated medical experts of the group consultants received the link of the new case through email. The medical experts open the new case with the help of link and gives their comments and save it in the proposed tool. Figure 6.10 shows the new case along with the comments from different medical experts.

Figure 6.11 show the case revise phase of the CBR technique where the medical prac- titioners ensured that the diagnosis solution of the new input case is accurate then new

86 Figure 6.10: Comments from Different Medical Experts case will be stored in a case repository.

Figure 6.11: Verified and Validate the Accuracy of the New Case Solution

Finally, the proposed KBCDSS tool stores this new case into the case repository or knowl- edge base for future utilization. Figure 6.12 shows the confirmation message to the medical practitioners that the new case is stored in the case repository and that will be available for future use.

87 Figure 6.12: New Case is Stored in the Case Repository or Knowledge Base

6.3 Measures for Evaluating Classifier Performance

Evaluation of a medical DSS is an important issue before introducing it into a day-to-day clinical environment. “Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians’ decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes”[Nykänen et al., 1991].

6.3.1 Evaluating Similarity Algorithm of Case Retrieval Phase of CBR Technique

According to [Watson, 1998] evaluation of a CBR system can be performed by verifica- tion tests (i.e. building the system right) and validation (i.e. building the right system). For the DSS, performance is evaluated by doing some preliminary verification tests and experimental work.

The prototype systems are developed in Microsoft .NET plateform using ASP.NET, C Sharp as front-end software and Microsoft SQL Sever RII is used as database of the pro- posed KBCDSS to evaluate the performance. Some of the verification techniques for CBR systems described in [Watson, 1998] are followed here. A detailed paper of the verification is available in [Ahmed et al., 2011]. The two basic verification techniques are: a - Check Retrieval Accuracy - If the feature value of new input case without any measurement error are entered into the system then the similarity of retrieved cases from case library will be of 100% match. b - Check Retrieval Consistency - If the feature values of new input case are some how missing or poorly measured then the similarity algorithm retrieved the cases on the basis of remaining available feature values. In that situation 100% similarity of

88 retrieved cases is not possible.

Performance of the classifier in term of accuracy could be evaluated by comparing it with the domain expert, where the actual purpose is to check how accurate or consistent the classifier in performing as compare to domain expert who deduce results and inferences without the assistance of any technological application.

Table 6.2 shows the accuracy achieved by the proposed system and compare it with the other systems.

Table 6.2: Accuracy Achieved by the Proposed Similarity Algorithm and The Other Systems

Author System Application Techniques Accuracy (year) Name Domain Used Result %

[Zia et al., KBCDSS Breast Cancer CBR 100% 2015a]

[Sharaf-El-Deen Breast Cancer, 99.33%, MDSs CBR, RBR et al., 2014] Thyroid disease 99.53%

[Guessoum Chronic RespiDiag CBR 85.72% et al., 2014] Pulmonary Disease

[Neshat et al., MDSs hepatitis disease CBR, PSO 94.58% 2012]

[McSherry, lymphography, 86.5%, Clinical DSS CCBR 2011] SPECT heart 84.3%

Breast Cancer, 99.5%, [Fan et al., 2011] MDSs CBFDT Liver Disorder 85%

[Chuang, 2011] MDSs Liver disease BPN, CBR 95%

(EWCBR, 77.7%, [Hsu et al., Hypertension UWCBR, Clinical DSS 91.9%, 2011] Detection NWCBR) with 92.8% GA Continued on next page 89 Table 6.2 – continued from previous page

Author System Application Techniques Accuracy (year) Name Domain Used Result %

[Lin and ANN, AHP and ILDM Liver disease 94.2% Chuang, 2010] CBR

[Chang et al., Breast Cancer, CBR, GA and 98.4%, Clinical DSS 2010] Fisher’s Iris FDT 98.9%

[Begum et al., IPOS Stress Diagnosis CBR, FL 94% 2009]

In case reuse phase, if the similar records are not found from the case repository or knowledge base than the support vector machine (SVM) predict the solution of the new input case. After the prediction of the solution of the new case, the process follow the same cycle which we discussed in figure 5.8 and figure 5.9 in chapter 5 respectively.

6.3.2 Evaluating the Performance of SVM Classifier we have used the following measures for assessing the performance of the SVM classifier. The classifier evaluation measures for evaluating SVM classifier are summarized as: "Ac- curacy (also known as recognition rate), Error Rate or Mis-classification Rate, Sensitivity (or recall), Specificity and Precision"[Han et al., 2011]. The performance of the classifier evaluated with the help of different measuring parameters and shows the resultant in the form of confusion matrix. Table 6.3: Measures for Evaluating SVM Classifier for True Class Value

S.No Measure Formula Calculation Recognition % TP 446 1 Sensitivity 97.4% P 458 FN 12 2 Error Rate (TP) 2.6% P 0 459 TN 228 3 Specificity 94.6% N 241 FP 13 4 Error Rate (TN) 5.4% N 0 240

Table 6.3 shows the calculation percentage per true class including True Positive Rates (TPR) and False Negative Rates (FNR). Table 6.4 shows the confusion matrix of the True

90 class and shows the calculated values by applying the different measuring parameters that identified in Table 6.3 to evaluate the performance of the SVM classifier for True class.

Table 6.4: Confusion Matrix – True Class

Predicted Class yes no Total

446 12 yes 97.4% 2.6% P True Class

13 228 no 5.4% 94.6% N

Total P0 N0 P + N

Table 6.5 shows the calculation percentage for predicted class including Positive Predic- tive Value (PPV)and False Discovery Rates (FDR).

Table 6.5: Measures for Evaluating SVM Classifier for Predicted Class Value

S.No Measure Formula Calculation Recognition % Precision (Positive TP TP 446 1 Predictive 97.2% = 0 Value(PPV)) TP + FP P 459 FP 13 2 Error Rate (PPV) 2.8% P 0 459 Precision (False TN TN 228 3 Discovery 95% = 0 Rate(FDR)) TN + FN N 240 FN 12 4 Error Rate (FDR) 5% N 0 240

Table 6.6 shows the confusion matrix for Predicted class and shows the calculated values by applying the different measuring parameters that identified in Table 6.5 to evaluate the performance of the SVM classifier for predicted class.

Table 6.7 shows the calculation percentage over the entire confusion matrix. Table 6.8 shows the percentage over the entire confusion matrix and shows the calculated values by applying the different measuring parameters identified in Table 6.7 that evaluate the

91 Table 6.6: Confusion Matrix – Predicted Class

Predicted Class yes no Total

446 12 yes 97.2% 5.0% P True Class

13 228 no 2.8% 95% N

Total P0 N0 P + N overall performance of the SVM classifier.

Table 6.7: Measures for Evaluating SVM Classifier for Overall Accuracy

S.No Measure Formula Calculation Recognition % TP + TN 446 + 228 1 Accuracy 96.4% P + N 699 FP + FN 13 + 12 2 Error Rate 3.6% P + N 699

Table 6.8: Confusion Matrix – Overall Accuracy

Predicted Class yes no Total

446 12 yes 63.8% 1.7% P True Class

13 228 no 1.9% 32.6% N

Total P0 N0 P + N

92 The overall accuracy of the SVM classifier for Breast Cancer medical data set is 96.4% and overall error rate is 3.6%.

6.4 Chapter Summary

In this chapter, we have discussed the experimental analysis of the proposed online knowl- edge based clinical decision support systems (KBCDSS) where we used breast cancer med- ical data set from UCI machine learning repository to generate the results. Also shows the measures for assessing the performance of the classifier.

The next chapter provides the detail discussion of the research work, conclusion and future direction of this research.

93 Chapter 7

Discussion, Conclusion and Future Directions

This chapter presents discussion of the whole research work that incorporate with research queries and identifies that how research contribution solve the research queries and re- search gap. After that provide the conclusion of research work and identify some future directions for the continuing of this research.

7.1 Research Discussion

With the expansionary revolution of technology there is an increasing inclination of em- ploying computer aided tools in clinical practices that could potentially support the de- cision making and increase the graph of accuracy in diagnosis and treatment suggestion stages. This would not only reduce the errors but also escalate the consistency of deci- sions. Clinical decision support systems were introduced to support the clinicians in their clinical practices and these systems merely perform their task up to some extent. The preliminary efforts to introduce the decision support systems were began around 1950’s afterward multiple approaches and algorithms were introduced to devised medical deci- sion support systems such as, Bayesian statistics, analytical-decision models, symbolic

94 reasoning, neural-networks, rule-based reasoning, fuzzy logic etc.

This research work is accomplished to study the significance of domain knowledge dur- ing decision making phase and to introduce an online knowledge based clinical decision support system. Different research queries were addressed during the research which is complimented with the solutions provided as the contribution of this research. Methods and strategies applied in the research are carefully examined and validated for executing the proposed system formally in medical domain.

Initially we have discussed the implication of domain knowledge for the purpose of decision making in medical field and then accompanied this with the technological influence that have innovate the concept of computerized decision support tools to aid clinical practices. Afterward the comprehensive details of the earlier proposed CDSS have been discussed into the literature that has potential to significantly impact the decision making process. So far we have identified the research gaps in chapter 2 which indicates there is a neces- sitate developing a CDSSs which is proficient to compete the challenges that were arose during the development and successful execution of CDSSs in everyday clinical practices. It is then followed by the research methodologies that are employed in developing pro- posed online knowledge base clinical decision support system. Afterward the design and structural hierarchy of the proposed system is being discussed in detail which consider- ably response to the mentioned research queries and in the end the proposed system is being validated using the Wisconsin breast cancer data set from UCI Machine Learning Repository.

Major research gaps that were addressed in this thesis are:

I– There is need to develop an online decision support system which can possibly combine clinicians on a single dais.

II– Current decision support system should be designed in such a way that it could be employed by medical experts of different diseases and have the proficiency to provide solutions to the multiple diseases.

III– Need to introduce a CDSS which pursues structural knowledge representation scheme for construction of knowledge base and its representation in a systematic

95 manner.

IV – Current CDSS should have strong reasoning methodology as an inference engine which can efficiently generate conclusions and results.

V– The user interface for the purpose of human computer interaction needs to be more users friendly.

Keeping in view the above research gaps the aim of the research is to develop a prototype that could fulfill these gaps efficiently. The methodology adopt in the development of KBCDSS is based on multiple methodology concept.

We have given answers to the research queries that were addressed in chapter 1, sec- tion 1.5 as the research contribution.

Architecture design of the proposed system: The proposed system of knowledge base clinical decision support (KBCDSS) is web based which has the capability to generate results for more than one disease. The proposed system follows a step wise procedure of obtaining specified medical knowledge that is pre- requisite in the decision making phases of patient care. The architecture of the proposed KBCDSS shown in figure 4.2 is primarily comprises of five Layers namely layer 1 that works as a potential source of knowledge or medical cases data gathering, layer 2 is knowl- edge acquisition layer, layer 3 is data warehouse server layer, layer 4 is knowledge base and inference engine layer and finally layer 5 is the graphical user interface environment for the purpose of interaction of the medical practitioners with the system.

Significant Features of Proposed System: The following are the significant features of the proposed system:

1 – It’s a web based architecture that has the capability to allow medical practitioners to gather round over a single dais to check and confirm their diagnosis.

2 – Earlier proposed systems were developed to assist clinicians for a particular disease whereas the knowledge base clinical decision support system is proficient to facilitate clinical experts of multiple diseases.

96 3 – The proposed online KBCDSS is a multiple disease diagnostic system that provides a single platform of different field of medical practitioners through web where they can confirm and authenticate their diagnosis regarding patient diseases evaluation. Medical Practitioners can upload their medical cases in online KBCDSS and store it in KB. The KB is responsible to store all the structural information of different diseases and the experts of specific disease dealt separately by means of employed inference mechanism.

4 – System follows the steps of knowledge data discovery for mining the knowledge. Data pre-processing steps of KDD process were used which update data into data warehouse from different potential sources of data.

5 – Once the data is being updated into the data warehouse it is then undergoing the knowledge acquisition phase [Turban et al., 2005]. For this purpose an algorithm for knowledge acquisition is proposed in our model.

6 – Next we have employed the data base management system used for the construction of knowledge base and representing that knowledge in an efficient way and developed an algorithm for the construction of knowledge base and its representation.

7 – Case based reasoning (CBR) and support vector machine (SVM) used as hybrid reasoning approach that act as an inference mechanism in our developed system.

i – New hybrid CBR cycle is being proposed in our research that are follows the following activities:

• Case retrieval algorithm is proposed that solves the problem of similarity measurement in case retrieval phase. The accuracy level of the proposed algorithm is 100% that is shown in table 6.2. Figure 6.6 and figure 6.7 shows the retrieval results that have shown in chapter 6. • Reinstantiation strategy is properly implemented in case reuse phase. This strategy basically copies the solution of the most similar case that was retrieved from the case retrieval phase of CBR technique. When the similar records not found in the KB, then SVM predict the solution of the new input case. • After that, concept of group clinical decision making is implemented in case revise phase of the CBR technique. In this phase, the new case along

97 with the expected solution is forwarded the group consultants of the same medical domain for the confirmation of the decision of the new input case.

• When they received three positive comments from the group consultants for the solution of the new input case then the medical consultant submit the new input case in the knowledge base or case repository for future use. This process is called case retain phase of the CBR cycle.

User Friendly Graphical User Interface: Users friendly graphical user interface is being used which simplifies the human computer interaction. With the help of GUI environment, the medical practitioners can interact with the proposed system easily and this application can easily implemented in their domain like hospitals, pathological labs etc.

7.2 Conclusions

This research work has been carried out to aid the clinical decision making process and to enhance the validity of decisions which as a consequence increase the quality of health care. CDSSs were developed to be the dynamic knowledgeable decision support systems with an aim to provide assistance to clinicians in their everyday clinical practices. Still the effective progression of these computer aided system is rare in the medical field due to their multifaceted nature. Keeping in view the research gaps that were addressed in this thesis we have identified the need to develop a decision support system that could efficiently fulfill the mentioned research gaps. We therefore conclude from the conducted research that the proposed online KBCDSS with its well organized structure and schematic hierarchy could be deployed as an effectual prototype in clinical domain that aid the process of diagnosis and treatment suggestion of diseases. Hence, this would not only increase the quality of decisions but also ensures the well being of human.

7.3 Future Research Directions

Some of the possible research directions are as follows:

• Automatic assigning weight for each feature values of the medical data sets and

98 automatic case adaptation process are important research area in medical domain.

• Develop a framework and effective inference mechanism that deals with unstruc- tured, semi-structured and structured data in medical domain.

• Develop a clinical decision support systems that could implement non-knowledge base techniques like neural network, genetic algorithm and support vector machine and then make comparison to the results of the proposed research work.

• Decision Support Systems that simultaneously incorporate surgical side and demon- strating through virtual reality in medical field would also be a future research direction to extend the confidence above decision making. Employ KB and effi- cient inference mechanism in Surgical Robotics Systems that helps surgeons during surgery.

• Due to increase usage of iPhone technology in different domain and specially in medical domain, design and develop an approach and methodology to implement the proposed research that is online KBCDSS in iPhone. With the help of iPhone application, the medical practitioners, pathologist and senior consultant take an advantage for their decision making process without using the computer systems.

99 Appendices

Appendix A – Overview of the Appended Papers

This section shortly summarizes the contributions of each paper.

Paper 1

Schematic Cycle of Case-Based Reasoning Technique Implements in Clinical Decision Support Systems Used for Diagnosis of Liver Disease. Syed Saood Zia, Pervez Akhtar, Tariq Javid Ali Mughal,Sindh University Research Journal (Science Series) 47(2) June 2015: 215-220. ISI index.

Paper Summary: This paper uses the four step schematic cycle of cbr technique for the diagnosis and classification purpose. Similarity computation is the major flaw to accomplish an efficient retrieval of cases from case library. We have proposed a similar- ity algorithm to answer this difficulty which employed the Average Weighted Euclidean distance method for this reason. After the retrieval phase we have used the case rein- stantiation strategy for case adaption phase. This proposed solution is then undergo the case revise phase of CBR system and revised by the experts. This revised solution is then finally retained into the case library as a new stored case. The conducted research performed data analysis on Indian Liver Patient data set from UCI Machine Learning Repository.

Major findings: We have implemented four phases of CBR technique by proposing simi- larity measurement algorithm for efficient retrieval of cases.

100 Paper 2

Case Retrieval Process of CBR Technique Implements on Knowledge-Based Clinical Decision Support Systems (KBCDSS) for Diagnosis of Breast Cancer Disease. Syed Saood Zia, Pervez Akhtar, Tariq Javid Ali Mughal,Sindh University Research Journal (Science Series) 47(2) June 2015: 241-246. ISI index

Paper Summary: This paper launched the concept of an online knowledge based clin- ical decision support system which is competent enough to gather medical consultants over a single platform through web from where they can confirm and validate their sug- gested findings about the patients. It’s a multiple disease oriented framework with a well planned and systematic structure which ensures its efficient progression in the medical field. Database management system is being employed in our proposed system to main- tain structural meta-repositories and as the representation scheme. In order to deduce results as an inference mechanism case base reasoning approach is the applied method- ology in our proposed KBCDSS and for the purpose of human computer interaction we have provided a user friendly GUI environment that will help in easy procession among clinicians. The proposed knowledge based clinical decision support system is deployed as an effective prototype in medical domain which is competent to support the medical decision making process more efficiently than previously developed CDSS.

Major findings: Implement Proposed Case Retrieval Algorithm on our porposed KBCDSS tool.

Paper 3

CBR: Cycle, Framework and Applications. Syed Saood Zia, Pervez Akhtar, Rashid Hussain, and Idris Mala,World Applied Sciences Journal 32, no. 7 (2014): 1349-1355.

Paper Summary: This paper is presenting an overview of the researches carried out on significant implementation of case base reasoning (CBR) approach in medical field. Com- prehensively outlines the CBR cycle and its framework along with it variety of medical CBR applications are also presented in this paper. We have summarized the compre- hensive details of the functioning of CBR system which is a four step schematic cycle and more specifically different similarity measurement algorithms have been discussed for

101 computing similarity and retrieval of cases. Next section of the paper introduced some of the earlier proposed frameworks of CBR technique and the medical applications that use CBR as the core methodology have also been discussed. This analysis reveals that the cognition based model of CBR approach is quite fundamental in medical domain for taking decisions during diagnosis and treatment of diseases.

Major findings: Analyze the significance of case base reasoning approach in medical field.

Paper 4

Case Retrieval Phase of Case-based Reasoning Technique for Medical Diagnosis. Syed Saood Zia, Pervez Akhtar, Tariq Javid Ali Mughal, and Idris Mala,World Applied Sci- ences Journal 32, no. 3 (2014): 451-458.

Paper Summary: The presented paper describes the detailed description of case retrieval phase of CBR technique that is to extract out the most similar cases and also identi- fied the similarity measurement issues that are interrelated with this retrieving process. Thorough review of the literature elaborate the four phases of CBR cycle and emphasize done on the case retrieval phase which involves the four major sub tasks to accomplish an efficient retrieving process. We have identified the importance of similarity metric in retrieving the most suitable case and different case retrieving algorithms have also been discussed. In this research paper we have introduced an average weighted Euclidean dis- tance method to compute the similarity among cases. Data analysis has been performed on the Wisconsin Breast Cancer data set from UCI Machine Learning Repository and to deduce the inferences used myCBR tool.

Major findings: Implement Case retrieval phase of CBR technique using myCBR tool in medical domain.

Paper 5

Clinical Decision Support System: A Hybrid Approach. Syed Saood Zia, Pervez Akhtar, Idris Mala, Abdul Rehman Memon,IEEEP Karachi Center, 27th Annual Multi-topic In- ternational Symposium 2012, Karachi, Pakistan.

102 Paper Summary: This presented paper briefly outlines our initially proposed framework of KBCDSS. The proposed system uses hybrid reasoning approach that are comprises of rule based reasoning and case based reasoning to enhance the accuracy in making diag- nosis.

Major findings: Proposed hybrid reasoning techniques increase the accuracy level in diagnosis.

103 Appendix B – Software Tools

In this appendix, the list of software tools are mentioned that are used for this research work. The following list shows the software tools that are used for the development of the proposed KBCDSS tool.

Software Tools used for the development of the Proposed System (KBCDSS)

Software Logo Software Name

Microsoft Visual Studio 2010

Microsoft ASP.net

Microsoft SQL Server R2

JQuery

Java Script

Cascade Style Sheet

Bootstrap

The following software tools are used for writing the thesis report.

Software Tools used for writing the Thesis

Software Logo Software Name

TexMaker – Latex Editor

Tex Works

104 These software tools are used for graphics.

Software Tools used for Graphics

Software Logo Software Name

Adobe Photoshop CS 3

MS Paint

105 Appendix C – Research Groups

Following are the research groups that are doing similar area of research.

1 Group Name: Clinical Decision Making Country: United States of America University: MIT Url: http://groups.csail.mit.edu/medg/

2 Group Name: Artificial Intelligence Research Group (AIRG) Country: United States of America University: Worcester Polytechnic Institute Url: http://web.cs.wpi.edu/Research/airg/

3 Group Name: Computational Intelligence Group Country: United Kingdom University: University of Kent Url: http://www.cs.kent.ac.uk/research/groups/compint/

4 Group Name: Artificial Intelligence Group Country: United Kingdom University: University of Cambridge Url: https://www.cl.cam.ac.uk/research/ai/

5 Group Name: Knowledge Engineering and Discovery Research Institute (KEDRI) Country: New Zealand University: Auckland University of Technology Url: http://www.kedri.aut.ac.nz/

6 Group Name: Intelligent Systems Country: Sweden University: MALARDALEN University Sweden Url: http://www.es.mdh.se/research groups/31Intelligent_Systems

7 Group Name: Machine Intelligence & Knowledge Engineering (MINE) Country: Kingdom of Saudia Arabia

106 University: King Abdullah University of Science & Technology (KAUST) Url: http://mine.kaust.edu.sa/Pages/Home.aspx

8 Group Name: Knowledge Technology Research Group Country: Malaysia University: Universiti Kebangsaan Malaysia Url: http://www.ftsm.ukm.my/kt/

9 Group Name: Intelligent Computing Country: Malaysia University: Universiti Putra Malaysia Url: http://fsktm.upm.edu.my/site/?pg=1-dept&dept = I C

107 Appendix D – Resume

Syed Saood Zia

F. B. Area, Karachi (75950), Pakistan.

Phone: +92-213-632-8180 Cell: +92-346-276-7788 Email: [email protected] Current position

Jul. 2014 - To Date Assistant Professor Department of Software Engineering Sir Syed University of Engineering & Technology Main University Road,75300 Karachi, Pakistan. Areas of specialization

Information System, Intelligent Computing and Artificial Intelligence. Education

Year of Degree Main Subject Name of University Division Passing

Computing & London Metropolitan B.Sc. 2000 2ndDivision Information Systems University

Khadim Ali Shah MCS Computer Science Bukhari Institute of 2005 1stDivision Technology

Mobile Computing & MS Hamdard University 2007 1stDivision Information Systems

108 Work Experience

Period Designation College / University

Lecturer & Incharge Jan. 2000 - May 2005 College of Digital Sciences Manager Projects

Jul. 2005 - Aug. 2008 Lecturer Commecs College

Sir Syed University of Engineering & Aug. 2008 - Jun. 2014 Lecturer Technology

Grants, honors & awards

2005 Gold Medalist MCS in Computer Science, KASBIT, Pakistan. 2007 2nd Position MS in Mobile Computing & Information Systems, HU, Pakistan.

109 Appendix E – Health-care Standards

Health Level -7 (HL7)

HL7 refers to a set of international standards for transfer of clinical and administrative data between software applications used by various health-care providers. These stan- dards focus on the application layer, which is "layer 7" in the OSI model. [Smith et al., 2013]

The HL7 standards are produced by the Health Level Seven International, an interna- tional standards organization, and are adopted by other standards issuing bodies such as American National Standards Institute and International Organization for Standardiza- tion. Hospitals and other health-care provider organizations typically have many different computer systems used for everything from billing records to patient tracking. All of these systems should communicate with each other (or "interface") when they receive new in- formation, or when they wish to retrieve information, but not all do so.

HL7 International specifies a number of flexible standards, guidelines, and methodologies by which various healthcare systems can communicate with each other. Such guidelines or data standards are a set of rules that allow information to be shared and processed in a uniform and consistent manner. These data standards are meant to allow health-care organizations to easily share clinical information. Theoretically, this ability to exchange information should help to minimize the tendency for medical care to be geographically isolated and highly variable.

HL7 International considers the following standards to be its primary standards – those standards that are most commonly used and implemented:

I– Version 2.x Messaging Standard – An interoperability specification for health and medical transactions.

II– Version 3 Messaging Standard – An interoperability specification for health and medical transactions.

110 III– Clinical Document Architecture (CDA) – An exchange model for clinical docu- ments, based on HL7 Version 3.

IV – Continuity of Care Document (CCD) – A US specification for the exchange of medical summaries, based on CDA.

V– Structured Product Labeling (SPL) – The published information that accompanies a medicine, based on HL7 Version 3.

VI – Clinical Context Object Workgroup (CCOW) – An interoperability specification for the visual integration of user applications.

Other HL7 standards/methodologies include:

• Fast Healthcare Interoperability Resources (FHIR) – A draft standard for the exchange of resources.

• Arden Syntax – A grammar for representing medical conditions and recommenda- tions as a Medical Logic Module (MLM).

• Claims Attachments – A Standard Healthcare Attachment to augment another health-care transaction.

• Functional Specification of Electronic Health Record (EHR) / Personal Health Record (PHR) systems – A standardized description of health and medical func- tions sought for or available in such software applications.

• GELLO – A standard expression language used for clinical decision support.

Clinical Data Interchange Standards Consortium (CDISC)

The Clinical Data Interchange Standards Consortium (CDISC) is an open, multidisci- plinary, neutral, 501(c)(3) non-profit standards developing organization (SDO) that has been working through productive, consensus-based collaborative teams, since its forma- tion in 1997, to develop global standards and innovations to streamline medical research and ensure a link with healthcare [Wood Jr and Fitzsimmons, 2001].

111 The CDISC mission is "to develop and support global, platform-independent data stan- dards that enable information system interoperability to improve medical research and related areas of health-care". The CDISC Vision is "informing patient care and safety through higher quality medical research". The CDISC suite of standards supports medical research of any type from protocol through analysis and reporting of results. They have been shown to decrease resources needed by 60% overall and 70–90% in the start-up stages when they are implemented at the beginning of the research process.

They are harmonized through a model that is now not only a CDISC standard but also an HL7 standard on the path to becoming an ISO/CEN standard, thus giving the CDISC standards (harmonized together through BRIDG) international status and accreditation.

Integrating the Healthcare Enterprise (IHE)

Integrating the Healthcare Enterprise (IHE) is a non-profit organization based in the US state of Illinois. It sponsors an initiative by the health-care industry to improve the way computer systems share information. IHE was established in 1998 by a consortium of radiologists and information technology (IT) experts.

IHE created and operates a process through which interoperability of health care IT systems can be improved. The group gathers case requirements, identifies available stan- dards, and develops technical guidelines which manufacturers can implement. IHE also stages "connectathons" and "interoperability showcases" in which vendors assemble to demonstrate the interoperability of their products.

IHE integration profiles describe a clinical information need or workflow scenario and document how to use established standards to accomplish it. A group of systems that im- plement the same integration profile address the need/scenario in a mutually compatible way.

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