Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 8, Number 1 (2015) pp. 53-65 © Research India Publications http://www.ripublication.com

An Ontology-Based Expert System for General Practitioners to Diagnose Cardiovascular Diseases

Baydaa Taha Al-Hamadani1, Raad Fadhil Alwan2

1 Assistant Professor, Department of Computer Science, Zarqa University, Zarqa 13132, Jordan. Tel: +962-77-5663578. E-mail: [email protected] 2 Associate Professor, Department of Computer Science, Philadelphia University, Aman 19392, Jordan. Tel: +962-77-7426528. E-mail: [email protected]

Abstract

According to World Health Organization, Cardiovascular diseases are the number one cause of death worldwide. Diagnosing the disease in the right time could lower the danger that it may cause. This paper presents an expert system to assist the General Practitioners (GP) to diagnose any kind of coronary artery diseases. The system supplies the experts with the diagnosing strategies that could be used and suggests the drugs and/or other required operations to be taken alongside with the explanation about the given decision. The design of the system depends on ontology knowledge about the patient’s symptoms to build the knowledge base and then it utilizes Semantic Web Rule Language (SWRL) to deduce the suitable medicine and the required operation for the patient. The system was tested by several GPs using 16 instances to test the validation and the evaluation of the system. Recall and precision factors were calculated 0.83 and 0.87 respectively which considered being reasonable values.

1. Introduction Although medical expert systems are challenging due to huge amount of knowledge and its complexity, the use of these systems witnesses a fast growing in recent years. This is because these systems reduce the time required to diagnose a specific disease. They also reduce the expected errors that a physician can made and minimize the amount of unnecessary laboratory tests. With all these advantages of such systems, these systems become familiar and vary from desktop programs to web services sites to mobile applications. In 1950s the first idea of developing an expert system has been proposed [1] and this idea is still in development thought the last decades. In early 2000s the idea of using

54 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan ontologies in the field of computer science discovered and emerged with different topics. Ontology is a way to represent the objects and the relations between these objects in a specific domain. Using ontology in medical domain is a hot issue. It is used to get the benefit from the existence of complex information that is involves in many health care activities. They are useful particularly in many computer applications such as information retrieval and natural language processing. It has an important role in building Decision Support Systems (DSS) since these systems depends on a large volume of knowledge which is difficult to capture and formalize [2]. Several works appear that use ontology to build expert systems in the field of biomedical, especially in diagnosing heart diseases. Heart diseases can be classified into Coronary artery, diseases of heart valves, cardiomyopathy, or birth heart defect. The Ontology-based expert systems focus on diagnosing some of these types of heart diseases. [3, 4] suggested two different systems to diagnose unstable angina and acute cardiac disorder respectively. Theses two disorders are considered as part of coronary artery diseases. [5], on the other side, implements their ontology system on diagnosing valve diseases. This paper presents a new ontology-based expert system which focuses on diagnosing all types of coronary artery diseases. Several ontology languages have been proposed to encode the knowledge about a specific domain. Ontology Inference Layer (OIL), Web Ontology Language (OWL), and Resource Description Framework (RDF) are examples of markup ontology languages. This work used OWL2, which is a W3C recommendation language at 2009, with its OWL-DL sublanguage and its Pallet reasoner. since it is more expressive than OWL-Lite and it has the abilities of automated reasoning, automatic computation and classification hierarchy, and check for inconsistencies for the designed ontology.[6] The main building block of any expert system is the knowledge base. In Ontology- based expert systems Semantic Web Rule Language (SWRL), another W3C recommendation, is a rule and logic expressive language that combines both OWL- DL and OWL-Lite with the rules markup language [7].

2. State of Art 2.1 Expert System Since the beginning of the using computers, scientist and physicians started to thing of ways to utilize computers in assisting them to do their work. The first article appeared in the field of diagnosing process was in 1959 [8] that suggested a technique that helps physicians in diagnosing diseases and focused on pointing the light on the benefits of using computers in medical fields. Moreover, several early systems appeared. The most well known system was developed in Leeds University in 1972 [9] to diagnose abdomen pain using Bayesian probability theory. Later in 1976, another well known medical diagnosing system appeared, MYCIN [10]. MYCIN embedded the field of Artificial Intelligent (AI), which uses abstract symbols rather than numerical calculations, to build the production rules and to strength the

An Ontology-Based Expert System for General Practitioners 55 reasoning process to identify bacteria causing severe infections. In 1991, A Dynamic Hospital Information System (HELP) appeared [11] that has the ability to generate alerts when abnormal signs in the patient record are noted. There are five main components in any rule-based expert system [12-14]. The knowledge base which has the rules and any other form of information collected from the human experts in the field. Knowledge can be either abstract or concrete. While abstract knowledge can be represented by rule and probability distribution, concrete knowledge refers to the information related to a specific abstract knowledge (facts). The heart of every expert system is the Inference Engine which draws conclusions by applying abstract knowledge to concrete knowledge. These conclusions can be based on either deterministic knowledge (knowledge about certain facts), probabilistic knowledge (knowledge about uncertain facts), or nondeterministic knowledge (fuzzy knowledge) [13]. Another component in the building blocks of an expert system is the Explanation Mechanism which provides the user with the necessary explanation about the way a specific conclusion drawn and the reasons behind using specific facts. When the expert system deals with users, it should be accompanied with a User Interface component which should be user friendly and easy to use.

2.2 Ontology In the field of computer science and information technology, Ontology is the process of representing knowledge in a specific domain [15]. Ontology is used to represent the sharable knowledge in terms of concepts which represented by classes, relations which represent the relations between the concepts, instances which are the objects represented by the concepts, and axioms which represent the rules that tie the concepts to the instances [16]. Fonseca [17] defines ontology as “an ontology refers to an engineering artefact, constituted by a specific vocabulary used to describe a certain reality”. He identified the difference between ontology and information systems in modelling and reasoning about information, as ontology deals with the information in conceptual level, while information systems do this task in implementation time. Rousey et.al. [18] gave different perspectives of the word “Ontology” in the field of Compute Science. “For example, ontology can be: a thesaurus in the field of information retrieval, a model represented in OWL in the field of linked-data, or a XML schema in the context of databases”.

2.3 Web Ontology Language (OWL) Among several ontology languages, OWL is the most used one. It is a W3C recommendation in 2004 to be “used by applications that need to process the content of information instead of just presenting information to humans” [19]. It has several features over XML and RDF by providing additional vocabulary along maintain their properties. It is then used to define instances (individuals) and maintain their properties, and then it is used to reason about these classes and individuals [18]. OWL has three sub-languages:

56 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan

1. OWL-Lite: This sub-language intended for users who need simple modelling and constraints. Although it provides quick path to thesauri and other taxonomy, its cardinality is limited to either 0 or 1. 2. OWL-DL: To fill the shortage of OWL-Lite, this sub-language comes with features that enrich the use of OWL. Class Boolean combinations and class property restrictions are some of the added features. Other properties in describing a class in term of other disjoint classes are another new feature. With all these features, OWL-DL becomes the most used language since it provides the user with full expressiveness [19]. 3. OWL-Full: this sub-language offers to its users maximum expressiveness and syntactic freedom of RDF[18]. As instance, OWL-Full treats a class as a set of individuals and as an individual at the same time. Its data type property generalizes to include inverse functional property.

2.4 Semantic Web Rule Language (SWRL) Based on the combination between OWL-DL and OWL-Lite sublanguages, SWRL was developed to be the rule language of the semantic web. It allows the users to write the first-order logic rules required to reason about the specified OWL individuals. The semantic rules of SWRL are the same as the description logic of Owl to make the reasoning process easier and stronger. Each SWRL rule has an antecedent and a consequent, each of which could be a disjunction of several atoms. There are several atom types that are supported y SWRL, such as class atoms, individual property atoms, data value property atoms, and data range atoms. The most powerful atoms are built-in atoms, where SWRL provides several types of existing built-in and allow the user to design and use his own built-ins [20].

2.5 Protégé Written in Java and using Swing packages, the Stanford Centre for Biomedical Informatics Research (BMIR) in Stanford University has developed Protégé as a platform for knowledge acquisition. Protégé is a flexible, user friendly environment used to create and modify Ontology systems in different fields. It allows its users to add plug-ins as they needed. It is supported with reasoners to check the consistency of the designed ontology and to infer new information base on the design of the existing system [21]. One of Protégé’s most important features is its extensibility to be integrated with other applications.

3. Building Ontology-Based Expert System 3.1 System Architecture The architecture of the proposed system consists of several modules. Figure 1 illustrates these modules and the interactions between them. As any other expert system, the core of our system is the knowledge base module which consists of the fact base and the rule base. The facts are extracted, using the user interface, from the user as the patient’s symptoms in addition to the laboratory and clinical test results.

An Ontology-Based Expert System for General Practitioners 57

Expert System Knowledge Base Fact base Inference Engine (Pellet Reasoner) Patient Symptoms User Interface Rule base Explanation Ontology Subsystem SWRL Classes Rules

Figure 1: The System Framework

The rules base consists of SWR decision rules and the ontology structured classes along with the relationships between these classes. The decision rules were inferred from technical guidelines published by the National Institute of Health and Care Excellence in the UK [22, 23]. While the ontology classes were formed using Protégé Ontology editor. The inference engine is the core of any expert system which depends on the facts and the rules to reason the required decision. In our work we use Pellet [24], which considered to be one of the best OWL-DL reasoner with several features such as data- type reasoning and debugging, rules integration, and reasoning conjunctive queries. In this stage more decision rules could be inferred and added to the list of available rule base. The final decision results will be introduced to the user through the user interface alongside with the explanation about this decision inferred from the explanation module.

3.2 System Methodology Several methodologies have been proposed to be used in building and developing ontology-based expert systems. One of these methodologies is METHONOLOGY, which is considered to be the most comprehensive ontology engineering methodology [25-27]. As illustrated in Figure 2 (developed from [4, 26]), METHONOLOGY does not specify only the stages that the development process should pass, but it also depicted the depth of some of these stages. Although the methodology seems to have several independent stages, most of these stages are interfere with each other and can be operate simultaneously. CardioOWL depends on this methodology and the stages of the development process are as follows:

58 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan

Define the purpose & Specify the scope

Ontology Building Capturing Knowledge Ontology Implementation Knowledge Conceptualizing Integration

Evaluation Maintenance

Documentation

Figure 2: Ontology development life-cycle.

1. Define the purpose and specify the scope stage: This stage should determine the purpose of building the ontology, its formality, and its scope. The purpose of CardioOWL is to be a knowledge representation of cardio vascular diseases domain embedded in an easy to use GUI. According to [28] the degree of formality of the ontology depends on the amount of formal knowledge against the natural language knowledge present in that ontology. Since CardioOWL was expressed in a formally defined language (OWL), its degree of formality is “semi-formal”. The proposed system can be used by the specialist in the field in order to get an accurate diagnosis based on the reasoning process embedded in the ontology. 2. Capturing knowledge stage: The depth of this stage start to be high in the beginning of the design process and it decreases as the process goes farther. Several techniques were used in capturing the knowledge required to build CardioOWL. Non-structured interviews with the experts in the field were useful to specify the requirement of theses experts, while structured interview were used to get the detailed knowledge and to specify the concepts, object properties, data properties and the relations between them. On the other hand, formal and informal text analyses were used in this stage to identify the main structure of the ontology and to fill the concepts in this structure respectively. 3. Knowledge conceptualization stage: This stage depends on the output of the previous stages by constructing the Glossary of Terms (GT) [26] which include the concepts and the properties used in designing the ontology. These terms are useful to construct the class hierarchy and to determine the

An Ontology-Based Expert System for General Practitioners 59

properties and the relations between these classes. First the conceptualization table should be formed as shown in Table 1. Next, this table should be transferred into a conceptualization tree. For this purpose we use the rich semantic tree (RST) suggested by [29]. Figure 3 shows the CardioOWL RST tree and the key shapes used in it. 4. Integration stage: This stage specifies if the developed ontology can have benefit from other existing ontologies. This process exemplifies the feature of ontology reusability, when the existing ones can be integrated in to a new developing one. CardioOWL integrate part of the ontology used in [30]. This part is the (Patient-Info) class (see Table 1) and some of its instances.

Patient

hasDisease treatedBy

Disease Treatment hasSymptom diagnosedBy

Diagnosing Symptoms Treatment Choices

Symptoms List Diagnosing Techniques

Non-expressed object Is sub-class of Expressed object Is part of Relationship set Optional participation

Figure 3: CardioOWL RST

5. Implementation Stage: This stage is responsible on implementing the ontology in one of the available ontology editors. CardioOWL used Protégé 4.0 as an editor which has the ability to check the lexical and syntax errors. Protégé is integrated with several types of reasoners which guarantee the completeness, consistency, and non redundancy in the defined ontology. The reasoner used by the developed ontology is Pellet [31]. Figure 4 shows the tree structure

60 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan

built by using OWL-Viz plug-in embedded with Protégé for CardioOWL ontology.

Table 1: CadioOWL conceptualization table Concept instances Value Name Diseases CoronaryArtery CoronaryArter:AcuteCoronary CoronaryArtery:StableAngina CoronaryArtery:HeartFailure Symptoms ArmPain Yes/No Breathlessness Yes/No ArmPain Yes/No ExtremeTiredness Yes/No ChestPain Yes/No ChestPain:Onrelax Yes/No ChestPain:Preasure Yes/No ChestPain:Severe Yes/No ChestPain:UnderStress Yes/No FeelFaint Yes/No FeelSick Yes/No Headache Yes/No LegSwelling Yes/No Sweating Yes/No Diagnosing ECG Pos/Neg EchoCG Pos/Neg StressTesting Pos/Neg Angiography Pos/Neg CardioMarkers Pos/Neg Treatment AngioPlastyWithStent CABG Drugs Drugs:Nitroglycerin Drugs:CholesterolLowering Drugs:BloodClotBreaking Drugs:BetaBlockers Patient-Info GeneralInfo GeneralInfo:PatientAge NonNegateiveInrtrger GeneralInfo:BloodType A, B, AB, O GeneralInfo:Sex M/F Smoker Yes/No FamilyHistory Yes/No DoExcercise Yes/No HealthyDiet Yes/No

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Protégé provides the abilities to create the concepts and their data properties and object properties in expressive description logic [32]. Protégé 3.x provides a nice environment to write Semantic Web Rule Language (SWRL), while the later versions (Figure 5) provide a very simple SWRL editor. 6. Evaluation: It is not a stand alone stage but it is the one that interfere with all the previous stages. As seen in the evaluation stage depth starts to be high at the first stages of the development process and then it decreases as the process goes farther. In the first stages the important thing is to guarantee the verification of the developed ontology which is done by checking the correctness of the captured knowledge and the conceptualizing table and its tree using interviews with the experts in the domain. While in the later stages, the validation of CardioOWL has been checked by the experts to make sure that the designed system meets their requirement. 7. Documentation: this stage is going in parallel with all other stages since the output of each stage is a document which is important to available as an input for the next stage. Moreover, publishing papers in journals or conferences in considered being a kind of documentation.

Figure 4: CardioOWL Tree structure produced by Protégé

62 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan

Figure 5: A screenshot of Protégé 4.3

4. Evaluation and Discussion The process of evaluating an expert system has two stages, technical evaluation and user evaluation. As mentioned in the previous sections, technical evaluation should pass through verification and validation. During the verification stage all the collected knowledge should be guaranteed to be correct, while during the verification stage the designed system should be guaranteed to work right. Several techniques were used to check the validation of an expert system and one of the most dominant ones is validation based on case studies [33]. Determining test instances is crucial to test the validation of the system. These instances should cover all the possible diagnosis at the moment of a given instance[25], and the test set used are taking from [34]. To test CardioOWL, 16 instances were defined and the system runs by four different general practitioners (GP) to ensure the generality of the designed system. Each one of the 16 test cases has input parameters which include the patient general-info, symptoms, and diagnosing techniques test results. The correct results obtained by the system after implementing all the instances were 100% correct. Calculating the precision and the recall, the factors used in the field of information retrieval, were used to check the validation of CardioOWL. retrieved results Pr ecision  total results

An Ontology-Based Expert System for General Practitioners 63

retrieved results Re call  correct results CardioOWL average precision is 0.872, while the recall is 0.831. The reason behind low recall is that the system inferred some extra results that are not needed as a result.

5. Conclusion and Future work The appearance of ontologies in the field of computer science affects the development of several types of systems and expert systems are one of them. This paper presents CardioOWL, an ontology-based expert system to help GPs and medical students to detect all types of coronary artery diseases. The system was tested by some GPs and their comments were taken into consideration to develop the system and guarantee its validation. The performance of the system was 100% correct and the precision and recall factors reflect the quality of the design. Considering fuzzy logic is one of the required developments on the system. Moreover, embedding a machine learning technique could enrich the system by making it learn from the result of the system and then use these results for further diagnosing. Upgrading the system to be a Web application is another development required.

Acknowledgment This research is funded by the Deanship of Research in Zarqa University /Jordan.

References

[1] Shaw, J.tag">C.N. and H.A. Simon. "Emperical Explorations of the Logic Theory Machine: a case study in heuristic". in Proceeding of the AFIPS Joint Computer Conferenes. pp 218-230. 1957 [2] Shortliffe, E.H., J.J. Cimino, M. Musen, B. Middleton, and R. Greenes, Clinical Decision-Support Systems, in Biomedical Informatics. Springer London. pp. 698-736.2006 [3] Vila-FrancÃs, J., J. SanchÃs, E. Soria-Olivas, A.J. Serrano, M. MartÃnez-Sober, C. Bonanad, and S. Ventura," Expert system for predicting unstable angina based on Bayesian networks". Expert Systems with Applications, vol. 40 no 12: pp. 5004-5010.2013 [4] Martínez-Romero, M., J. Vázquez-Naya, J. Pereira, M. Pereira, A. Pazos, and G. Baños3," The iOSC3 System: Using Ontologies and SWRL Rules for Intelligent Supervision and Care of Patients with Acute Cardiac Disorders". Computational and Mathematical Methods in Medicine, vol. 2013 no 1: pp. 13.2013 [5] Mork, P. and P.A. Bernstein. "Adapting a generic match algorithm to align ontologies of human anatomy". in Proceeding of the 20th International Conference on Data Engineering. pp. 2004

64 Baydaa Taha Al-Hamadani, Raad Fadhil Alwan

[6] Horridge, M., S. Jupp, G. Moulton, A. Rector, R. Stevens, and C. Wroe, A Practical Guide To Building OWL Ontologies Using Prot´eg´e 4 and CO-ODE Tools Edition 1.1. 2007, The University Of Manchester. [7] http://www.w3.org/Submission/SWRL/. [8] Ledley, R.S. and L.B. Lusted," The Use of Electronic Computers to Aid in Medical Diagnosis". Proceedings of the IRE, vol. 47 no 11: pp. 1970- 1977.1959 [9] Dombal, F.T.d., D.J. Leaper, J.R. Staniland, A.P. McCann, and J.C. Horrocks," Computer-aided Diagnosis of Acute Abdominal Pain". Br Med J., vol. 2 no 5804: pp. Pages: 9-13.1972 [10] Shortliffe, E., Computer-Based Medical Consultations: MYCIN: Elsevier. 264 pages. 1976, [11] Gilad, J.K., M.G. Reed, and T.A. Pryor, HELP: A Dynamic Hospital Information System: Springer Publishing Company, Incorporated. 357. 2012, [12] Leondes, C.T., Expert systems: the technology of knowledge management and decision making for the 21st century. Vol. Volume 6: Academic Press- Expert systems (Computer science). 1947 pages. 2002, [13] Castillo, E., Expert Systems and Probabilistic Network Models: Springer Science & Business Media. 1997, [14] Jackson, P., Introduction to Expert Systems. third edition ed: Addison- Wesley. 1999, [15] Gruber, T.R.," A Translation Approach to Portable Ontology Specifications". Knowledge Acquisition, vol. 5 no 2: pp. Pages 199-220.1993 [16] Davies, J., Lightweight Ontologies, in Theory and Applications of Ontology: Computer Applications, M. Healy, R. Poli, and A. Kameas, Editors., Springer Science & Business Media.2010 [17] Fonseca, F.," The Double Role of Ontologies in Information Science Research". Journal of the American Society for Information Science and Technology, vol. 58 no 6: pp. Pages 786-793.2007 [18] Roussey, C., F. Pinet, M.A. Kang, and O. Corcho, An Introduction to Ontologies and Ontology Engineering, in Ontologies in Urban Development Projects (Advanced Information and Knowledge Processing), G. Falquet, C. Métral, and J. Teller, Editors., Springer-Verlag London Limited.2011 [19] http://www.w3.org/TR/owl-features/. [20] Gil, Y., E. Motta, V.R. Benjamins, M. Musen, M. O'Connor, H. Knublauch, S. Tu, B. Grosof, M. Dean, W. Grosso, and M. Musen, Supporting Rule System Interoperability on the Semantic Web with SWRL, in The Semantic Web - ISWC 2005. Springer Berlin Heidelberg. pp. 974-986.2005 [21] http://protege.stanford.edu/products.php. [22] Guidline, N.C., Management of chronic heart failure in adults in primary and secondary care. 2010, National Institute for Health and Care Excellence UK.

An Ontology-Based Expert System for General Practitioners 65

[23] Guidline, N.C., Management of Stable Angina. 2012, National Institute for Health and Care Excellence: UK. [24] Sirin, E., B. Parsia, B.C. Grau, A. Kalyanpur, and Y. Katz," Pellet: A practical OWL-DL reasoner". Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5 no 2: pp. 51-53.2007 [25] Garcia-Crespo, Ã.n., A. RodrÃguez, M. Mencke, J.M. Gómez-BerbÃs, and R. Colomo-Palacios," ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements". Expert Systems with Applications, vol. 37 no 3: pp. 2621-2628.2010 [26] Ferndndez, M., A. Gomez-Perez, and N. Juristo, METHONTOLOGY: From Ontological Art Towards Ontological Engineering, A.T.R. SS-97-06, Editor. 1997. [27] Heijst, G.v., A.T. Schreiber, and B.J. Wielinga," Using explicit ontologies in KBS development". Int. J. Hum.-Comput. Stud., vol. 46 no 2-3: pp. 183-292.1997 [28] Uschold, M. and M. Gruninger," Ontologies: principles, methods and applications". The Knowledge Engineering Review, vol. 11 no 02: pp. 93- 136.1996 [29] Domingue, J., C. Anutariya, S. Lynn, and D. Embley, Semantically Conceptualizing and Annotating Tables, in The Semantic Web. Springer Berlin Heidelberg. pp. 345-359.2008 [30] Arsene, O., I. Dumitrache, and I. Mihu," Medicine expert system dynamic Bayesian Network and ontology based". Expert Systems with Applications, vol. 38 no 12: pp. 15253-15261.2011 [31] http://clarkparsia.com/pellet/features#rules. [32] Jovic, A., M. Prcela, and D. Gamberger. "Ontologies in Medical Knowledge Representation". in Proceeding of the ITI 2007. 29th International Conference on Information Technology Interfaces. pp 535-540. 2007 [33] O'Keefe, R.M. and D.E. O'Leary," Parforming and Managing Expert System Validation". Advanced in Expert Systems Management, vol. 1 no: pp. 141.1993 [34] Ghosh, J.K. and M. Valtorta, Building a Bayesian network model of heart disease, in Proceedings of the 38th annual on Southeast regional conference. 2000, ACM: Clemson, South Carolina.