An Ontology-Based Expert System for General Practitioners

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 Alwan

2

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:


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.


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:

Expert System

Inference Engine

(Pellet Reasoner)

Explanation

Subsystem

Knowledge Base

Fact base

Patient Symptoms

Rule base

SWRL

Rules

Ontology

Classes

User Interface

Figure 1: The System Framework


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

Figure 2: Ontology development life-cycle.

Documentation

Maintenance

Evaluation

Ontology

Building

Define the purpose &

Specify the scope

Capturing Knowledge

Knowledge Conceptualizing

Ontology Integration

Implementation


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.

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

Figure 3: CardioOWL RST

diagnosedBy hasSymptom

treatedBy hasDisease

Disease

Symptoms

Patient

Treatment

Diagnosing

Symptoms List Diagnosing Techniques

Treatment Choices

Is sub-class of

Is part of

Optional participation

Expressed object

Non-expressed object

Relationship set


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

ontology.

Table 1: CadioOWL conceptualization table

Concept Name

instances Value

Diseases CoronaryArtery CoronaryArter:AcuteCoronary CoronaryArtery:StableAngina CoronaryArtery:HeartFailure

Symptoms ArmPain Breathlessness ArmPain ExtremeTiredness ChestPain

ChestPain:Onrelax ChestPain:Preasure ChestPain:Severe ChestPain:UnderStress

FeelFaint FeelSick Headache LegSwelling Sweating

Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No

Diagnosing ECG EchoCG StressTesting Angiography CardioMarkers

Pos/Neg Pos/Neg Pos/Neg Pos/Neg Pos/Neg

Treatment AngioPlastyWithStent CABG Drugs

Drugs:Nitroglycerin Drugs:CholesterolLowering Drugs:BloodClotBreaking Drugs:BetaBlockers

Patient-Info GeneralInfo GeneralInfo:PatientAge GeneralInfo:BloodType GeneralInfo:Sex

Smoker FamilyHistory DoExcercise HealthyDiet

NonNegateiveInrtrger A, B, AB, O M/F Yes/No Yes/No Yes/No Yes/No


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


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.

resultstotal

resultsretrievedecisionPr


resultscorrect

resultsretrievedcallRe

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.

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An Ontology-Based Expert System for General Practitioners