Temporal Logic-Based Fuzzy Decision Support System for Diagnosis of Rheumatic Fever and Rheumatic Heart Disease
Total Page：16
File Type：pdf, Size：1020Kb
Temporal Logic-based Fuzzy Decision Support System for Diagnosis of Rheumatic Fever and Rheumatic Heart Disease Sanjib Raj Pandey Department of Computing and Information Systems Faculty of Architecture, Computing and Humanities University of Greenwich London, UK. A thesis submitted in partial fulfilment of the requirement of the University of Greenwich for the Degree of Doctor of Philosophy. Date: June 2016 DECLARATION “I certify that this work has not been accepted in substance for any degree and is not concurrently being submitted for any degree other than that of Doctor of Philosophy being studies at the University of Greenwich. I also declare that this work is the result of my own investigations except where otherwise identified by references and that I have not plagiarized from the work of others” Sanjib Raj Pandey Date Dr. Jixin Ma (1st Supervisor) Date Prof. Choi-Hong Lai (2nd Supervisor) Date 1 DEDICATION In loving memory of my grandfather Padma Dhoj Pandey, grandmother Tikka Kumari Pandey, grandmother Oaj Kumari Pandey and brother Suraj Raj Pandey; all of them would have been very proud of this achievement. 2 ACKNOWLEDGEMENT Firstly, I would like to express my deep gratitude to my research supervisors Dr. Jixin Ma and Professor Choi-Hong Lai for all their guidance, encouragement, sincere interest in a foreign research project, trust and for never losing their positive attitude. Without them, I would not have embarked on this research. I would like to thank my external advisor Dr. Praksh Raj Regmi (Executive Director of Rheumatic Fever and Rheumatic Heart Disease Prevention and Control Program, Nepal) and all the experts and users (community rural health workers) from the Nepal Heart Foundation, Nepal. Without their interest, guidelines, suggestions and assistance, the work presented in this thesis would not have been possible. I would like to express my big thanks to Mr. Peter de Vere Moss for his encouragement, technical support and valuable suggestions, without which I would not have been able to produce this thesis. I would also like to thank Dr. Chiyaba Njovu, for the long research discussion and encouragement from the time I started my research. Further, my wife Suman Pandey, helped me in many different ways and this has made me realise how important she is in my life. Finally, I want to thank all my family and friends for their moral support. A special thank you to my father Kalu Raj Pandey and mother Sita Pandey for their love, support and encouragement. 3 ABSTRACT This is a collaboration project between the Nepal Heart Foundation (NHF) and the University of Greenwich (UoG), United Kingdom (UK). Our mutual aim, agreed at the outset, has been to analyse, design and develop a cost effective Clinical Decision Support System (CDSS) for diagnosis and recognition of Acute Rheumatic Fever (ARF) and Rheumatic Heart Disease (RHD) at an early stage by developing/adopting UK’s and NHF’s treatment practices and procedures that would be appropriate for the Nepalese environment and lifestyle. The Application we developed was designed for use by community health workers and doctors in the rural areas of Nepal where laboratory facilities, expert services and technology are often deficient. The research undertaken investigated three problems that previously had not been addressed in the Artificial Intelligence (AI) community. These are: 1) ARF in Nepal has created a lot of confusion in diagnosis and treatment, due to the lack of standard procedures; 2) the adoption of foreign guidelines is often not effective and does not suit the Nepali environment and lifestyle and 3) the value of combining (our proposed) Hybrid Approach (Knowledge-based System (KBS), Temporal Theory (TT) and Fuzzy Logic (FL)) to design and develop an application to diagnose ARF cases at an early stage in English and Nepali. This research presents, validates and evaluates a proposed Hybrid Approach to diagnose ARF at three different stages: 1) Detected; 2) Suspected and 3) Not-detected and also to identify the severity level of detected ARF in the forms of Severe Case, Moderate Case or Mild Case. The Hybrid Approach is a combination of the KBS/Boolean Rule Model, Temporal Model and Fuzzy Model. The KBS/Boolean Rule Model has four components for design and implementation of KBS. These are: identifying the ARF stage in a case; Rule Pattern Matching; New Rule Formation and Rule Selection Mechanism. The Temporal Model has four components namely: Descriptive Explanation of ARF symptoms; Temporal Lookup-Table/Rules and Temporal Reasoning which produce a Temporal Template for demonstrating the relationship between the signs and ARF. The Fuzzification, Fuzzy Inferences and Defuzzification components are applied to design and implement a Fuzzy Model. The research undertaken divided the overall ARF diagnosis problem, in effect its requirements, into several sub-problems and each model of the Hybrid Approach 4 addressed particular sub-problems for example, Identify the stage of the ARF component of the KBS/Boolean Rule Model used to solve the question of identifying the stage of ARF based on the symptoms presented. Each problem was therefore handled using a particular model’s components. This significantly helped to improve maintainability, reliability and the overall quality of our final ARF Diagnosis Application. The developed ARF Diagnosis Application was experimentally tested and evaluated by NHF’s experts and users through applying NHF’s data sets consisting of 676 real patients’ records. The ARF Diagnosis Application was found to match 99% of the cases derived from NHF’s datasets. The overall ARF diagnostics performance and accuracy was 99.36%. Therefore, the experiments and evaluations of our ARF Diagnosis Application proved that it was logically and technically feasible to employ the Hybrid Approach for developing a new and practical ARF Diagnosis Application. The Application was ultimately developed and succeeded in embracing NHF’s requirements and guidelines thereby matching the Nepalese setting and making it suitable for use in Nepal having fully by met the exigencies of the Nepalese environment and lifestyle. Application of a new ARF diagnosis system (ours) proved that the Hybrid Approach, applied methods of diagnosis of ARF, medication and treatment plan, including help and supporting information which were identified and defined, were shown to be appropriate to support effectively community health workers and doctors who actively care for ARF and RHD cases in rural Nepal. 5 TABLE OF CONTENTS 6 LIST OF TABLES 13 LIST OF FIGURES 15 ABBREVIATIONS 18 AWARD & PUBLICATIONS 275 APPENDIXES 275 TABLE OF CONTENTS Chapter 1: Introduction .................................................................................. 20 1.1. Introduction ............................................................................................... 20 1.2. Country Background ................................................................................. 20 1.3. Problem Justification ................................................................................. 22 1.4. Research Overall Goal .............................................................................. 24 1.5. Research Questions ................................................................................... 24 1.6. Research Objectives .................................................................................. 25 1.7. Research Significance ............................................................................... 26 1.8. Road Map of this Thesis ........................................................................... 27 Chapter 2: Literature Review ......................................................................... 29 2.1. Introduction ............................................................................................... 29 2.2. Acute Rheumatic Fever and Rheumatic Heart Disease ............................ 29 2.3. Consequences of ARF ............................................................................... 32 2.4. Causes of ARF .......................................................................................... 32 2.5. Symptoms of ARF and RHD .................................................................... 33 2.5.1. World Health Organization ................................................................... 34 2.5.2. National Health Service: NHS Choice .................................................. 36 2.5.3. Medscape ............................................................................................... 37 2.5.4. Australia ................................................................................................ 37 2.5.5. New Zealand ......................................................................................... 38 2.6. Diagnosis of ARF ...................................................................................... 38 2.6.1. WHO and Duckett Jones criteria for diagnosis of ARF ........................ 38 2.6.2. Australia’s Guidelines ........................................................................... 39 2.6.3. New Zealand’s Guidelines .................................................................... 39 6 2.7. The Status of ARF and RHD in Nepal ...................................................... 39 2.8. Decision Support System .......................................................................... 41 2.8.1. Types and Components of DSS ............................................................ 43 2.9. Clinical