Software Development for Blood Disease Expert System

Abstract—In this paper, an expert system that evaluates the

software development for online blood disease expert system is

studied. The system asks 13 critical questions about the blood

tests and finally the system decides the type of the blood disease.

The data is obtained from Kirkuk General Hospital and the

system is tested on medical students from the Kirkuk University

School of Medicine. The results are satisfactory after it is tested

with 100 cases. The system is fully accurate after the diagnosis of

15 different diseases and the system is easy to use also its offer

time saving. The system is built by using forward chaining

methods the rules and parameters are determined due to a

predefined knowledge based and utilized in the expert system

called, Software development for blood disease Expert Systems

prepared by the software EXSYS CORVID. This expert system

can be used in determining the blood disease in patients and it

can be used in hospitals and universities by the physicians and

medical students etc.

Index Terms—Blood, disease, expert system.

I. INTRODUCTION

Blood disease is the most dangerous disease that infects

millions of people around the world. The disease that affects

the blood is called blood disease. These types of diseases

such as blood diseases, heart diseases, cancer and eye diseases

etc. needs continuous follow-up, those which are done by

computer application expert systems and online diagnosis

systems for these diseases. There are few types of artificial

intelligence that have practical application and expert systems

are the most frequently used one. Expert systems is the

computer program that uses rules and facts to diagnosis,

classification and solve problems in different fields and built

applications in medical , engineering , physics and chemistry

etc.

Medical expert system is an important expert system

because they are used to save lives of millions around the

world by prompting the service to the user and providing cost

savings because the user can use the systems at home. There

are two stages to build medical expert systems. In the first

stage; the medical background of the blood diseases is

collected by interviewing with doctors and the specialists. In

the second stage, the information that collected are represent

by rules and facts consist from IF and THEN parts, the IF part

presenting the reasons or symptoms and THEN part contain

the disease that recognized [1].

In this paper, an expert system for blood disease diagnosis

has been developed and implemented using CORVID

EXSYS expert system development tool. The system

Manuscript received April 22, 2015; revised January 5, 2016.

The authors are with the Yıldırım Beyazıt üNiversitesi, Çankırı cad.

Çiçek Sok No: 3, Ulus-Altındağ, Ankara, Turkey (e-mail: 125101418@

ybu.edu.tr, [email protected], [email protected]).

identifies number of diseases such as acute myeloid leukemia,

acute lymphocytic leukemia, chronic lymphocytic leukemia,

chronic myeloid leukemia bleeding& hemolysis, myeloid

aplasia, sidro plastic anemia, iron store adequate, anemia of

chronic disease, alpha thalassemia trait, beta thalassemia trait

and liver disease after some questions are asked about the

results of blood tests the system is diagnosis the type of

disease the patient was injured by. In the recent years, the

blood disease and other diseases such as cancer increased

clearly, so it’s necessary to build this type of systems for the

treatment of these diseases. The determination of the type of

the blood disease early is important for the doctors for the

treatment of these diseases and save the lives of patients.

These information used in software development for blood

disease expert system are acquired from Kirkuk General

Hospital and the system is tested by the Kirkuk University

School of Medicine students and Department of Hematology

in Kirkuk General Hospital and it’s gave the expected results

after it’s tested with 100 cases.

II. LITERATURE REVIEW

Medical expert system is one of the most commonly used

expert systems thanks to its quick servicing to distant places

and its cost saving quality. In the last decade, the use of the

medical expert systems has increased because of the observed

increase in the diseases that require seriousness care such as

blood disease, cancer, heart diseases etc.

Dendral was the first medical expert system developed by

the AI researcher Edward Feigenbaum and the geneticist

Joshua Lederberg beginning with 1960. This project

consisted of research on two main programs Heuristic

Dendral and Meta-Dendral [2].

MYCIN was blood disease expert system was

developed in the Stanford University at 1970 within five or

six years by Edward Shortliffe as doctoral dissertation using

lisp and more than 500 rules the system asked question to the

user and the user response as Yes/No. MYCIN has never been

used in practice because it’s difficult and not useable but it

gives high performance in the diagnosis of diseases another

weakness of MYCIN its conclude the result nearly within

twenty minutes [3].

In the field of genetics, the MOLGEN is the most famous

expert system that supply intelligence consultant to molecular

geneticist on the designing of experiments include the

treatment of DNA. For changing DNA material the geneticist

has different types of available laboratory techniques

(deletions, insertions, cuts, joins), different techniques for

defining the biological consequences of the changes, different

tools for measuring effects [4].

Heart Disease Program (HDP) is a large medical expert

system program for the treatment of heart diseases. Doctors

Software Development for Blood Disease Expert System

Ahmad Mozaffer Karim, Fatih Vehbi Çelebi, and Adnan Saher Mohammed

Lecture Notes on Software Engineering, Vol. 4, No. 3, August 2016

179doi: 10.18178/lnse.2016.4.3.246

mailto:[email protected]

enter case information about laboratory test; age and the

program determine the name of heart disease [5].

INTERNIS was educational project developed at the

University of Pittsburgh in the early 1970. The aim of this

system is to diagnose internal diseases, the rules that

represented in this system is extract from one expert Dr. Jack

Myers internal medicine chairman in the university of

Pittsburgh School of Medicine. One of the weaknesses of the

INTERNIS expert system is that it’s very slow and it can

conclude the consultation between19-30 minute, so it’s very

long [6]-[8].

The researcher in the field of expert systems can be noted

that many diseases have an expert system for diagnosis or

classification its types, but these systems are different in their

efficiency and the number of diseases they classify. For

example there is number of medical expert systems in the field

of blood diseases such as MYCIN expert system which

includes most type of blood disease, but it’s difficult to use as

we mentioned above. So we decided to build blood disease

expert system (BDES) the feature of this system it’s easy to

use and gives the results immediately.

III. EXPERT SYSTEM STRUCTURE

Expert system structure is shown in the Fig. 1. The main

components of expert system are knowledge base, inference

mechanism (engine) and user interface and knowledge

acquisition.

IV.

Fig. 1. Expert system structure.

Knowledge base

In this survey we aim to identify the mediating effect of

learning orientation on the relationship between leadership

style and firm performance. To test the propositions, a field

survey using questionnaires was conducted [9].

Inference engine

It functions like an engine, directs the search in different

directions in the knowledge base up to answer to the question.

An inference engine can be either simple or complicated,

depending on the structure of the knowledge base. There is

number of techniques used in inference engine forward

chaining, backward chaining and bi-direction [10].

Explanation

This mode allows the system to explain its conclusions and

its reasoning process. This ability comes from the AND/OR

trees created during the production system reasoning process

[11].

User interface

The user interface is communicated between the user and

the system, Success of any expert system depends on the

quality and the easiest to implement of user interface [12].

V. KNOWLEDGE ACQUISITION

Knowledge acquisition means getting knowledge of a

particular domain from some source, usually human, and

building it into a computer system. And it’s one of the difficult

tasks facing expert system developer. Transferring expert

expertise is not an easy task [13]. To understand the problem

of knowledge transferring, it is important to understand how

expert deals with his expertise.

The knowledge acquisition can be classified into three

categories: manual, semiautomatic and automatic.

Manual methods: In this method, the knowledge engineer

extracts expert knowledge and then codes it in a suitable

format. There are many known manual methods such as

interviewing, protocol analysis, observation; Manual

methods are slow, expensive, and sometimes inaccurate.

Therefore, there are some efforts for automating the

process possible [14].

Semiautomatic method: In this method, some of the

knowledge acquisition is acquired automatically without

some process. It allows the experts to build their own

knowledge bases with or without the assistance from the

knowledge engineers [14].

Automatic methods: In this method, the roles of both the

expert and the knowledge engineer are minimized or

even eliminated. Knowledge acquisition is made by the

plan architect analyst. The process of using computers to

extract knowledge from data is called knowledge

discovery [15].

VI. BLOOD DISEASE EXPERT SYSTEM (BDES)

A. Systems Goal

There are three main goals of (BDES) system. The first

one is that the systems diagnosis 15 diseases in high

performance. The second goal is the system offer time saving

within one minute the BDES system can conclude the

results .The last feature it’s easy to use and the last feature is

not available in the best previous blood disease expert

systems like MYCIN that is not usable and not used by the

normal user.

B. System Design

The Blood Disease Expert System (BDES) is a large

diagnostic online program covering most areas of blood

disease. The physician can enter patient information about the

history, physical examination, and laboratory tests, and then

the program generates detailed explanations of differential

diagnoses indicating the clinical data items which support

each diagnosis. BDES is rule-based expert systems used

forward chaining techniques to return the inferences from

knowledge base. This system consists of 15 rules each of

which consists of two parts. The IF part contains the results of

blood tests and THEN part contains the disease that

recognized according to the tests results. These rules

acquisitions from KIRKUK University hospital physician and

Knowledge

Engineer Knowledge

acquisition Knowledge

Bse

Explation Inference

engine

User

Interface

Expert

User


180

they are tested and used by hospıtal doctors and students of

the medicine college.

TABLE I: BDES RULES

NO Rules …

1

IF (Increase W.B.C )

AND (Presence of blast cell and B.M failure)

AND ( More than 5 year)

THEN (Acute

myeloid

leukemia)

2

IF (Increase W.B.C)

AND (Presence of blast cell and B.M failure)

AND (Least than 5 year)

THEN (Acute

lymphocytic

leukemia)

3

IF( Increase W.B.C)

AND (Absence of blast cell and B.M failure)

AND (Presence mature lymphocyte in bm

(peripheral blood) )

THEN

Chronic

lymphocytic

leukemia

4

IF (Increase W.B.C)

AND (Absence of blast cell and B.M failure)

AND (Myelo proliferative disorder)

THEN

Chronic

myloid

leukemia

5

IF (Decrease R.B.C)

AND (MCV high >98 fl)

AND (Hyper Segmented)

THEN Drugs

Cytotoxic

Agent

6

IF (Decrease R.B.C)


AND ( Poly Chromatic high reticulocyte

count)

THEN

Bleeding &

hemolysis

7

IF (Decrease R.B.C)


AND (Target cell Somatocyte)

THEN Liver

Disease

8

IF (Decrease R.B.C)


AND (Dysplasiacytopenia)

THEN

Myelodysplas

ia

9

IF (Decrease R.B.C)

AND (MCV high >98 fl) AND (Dimorphic)

THEN (Sedro

plastic

anemia)

10

IF (Decrease R.B.C) AND

(MCV Normal (78_98 fl) or Low (< 76 fl) )

AND (Normal or Reticulocyte account)

AND (Target Basophilic stippling) AND

(Increased HB A2)

THEN Beta

Thalassemia

Trait

11



AND (Normal or Reticulocyte account)

AND (Target Basophilic stippling)

AND (Normal HB A2)

THEN Alpha

Thalassemia

Trait

12



AND (Normal or Reticulocyte account) AND

(Dimorphic)

THEN

Sideroplastic

13




(Low ferritin consideration)

THEN

Anemia of

Chronic

Disease

14




(Normal or High ferritin consideration)

THEN Iron

Store

Adequate

15



AND (High Reticulocyte account)

THEN

Bleeding &

Haemolysis

BDES implemented by using EXSYS CORVID because its

powerful expert systems software and offer ability to build

online expert systems applications and it has very easy user

interface. The rules that mentioned above implemented by

asking 13 questions to the user to diagnosis 15 blood diseases:

Q1: What is the result of blood film?

Q2: What is the result of MCV?

Q3: What is the result of blood film and reticulocyte

count??

Q4: What about the reticulocyte case?

Q5: What about the reticulocyte count?

Q6: What about morphology of cell?

Q7: What about HB electro phoresis??

Q8: What about ferritin consideration??

Q9: What about age?

Q10: Case of hypochromia?

Q11: Case of the normal or low reticulocyte count?

Q12: Presence mature lymphocyte or myeloproloferative?

Q13: Presence of blast cell in peripheral blood and bone

marrow failure?

The Number of questions asked to the user different from

one case to another after the system asked its questions it’s

concludes the type of disease. For example to improve the

system efficient we implement the first rule. The blood film

increase and Presence of blast cell in P.B+B.M failure and the

age is 50 years.

As shown in Fig. 2, the first screen consists of the definition

goal of the system.

Fig. 2. First window in run time.

Fig. 3. First question.

As Shown in Fig. 3 the first question that asked to the user

is about result of blood disease film, and there is two chances

assume that the first one increase of White Blood Disease was

chosen.


181

The second question asked to the user is shown in Fig. 4 is

about Presence of blast cell in peripheral blood and bone

marrow Failure, also there is two answers the first one is

chosen.

Fig. 4. Second question.

The Third question asked to the user is shown in Fig. 5

about age of the case also the first answer is chosen according

to the first rule in the Table I: BDES Rules.

Fig. 5. Third question.

As shown in the Fig. 6 the system concludes the disease is

(Acute myeloid leukemia) and that’s expected result

according to the first rule in the Table I: BDES Rules. From

this case we can conclude that the BDES expert system offer

both the time saving and the usability these two features are

not offer in the MYCIN which is one of the most known blood

disease expert system.

Fig. 6. BDES decision screen.

VII. CONCLUSION

In this paper, an expert system that evaluates the software

development for online Blood Disease Expert System is

studied. The system asks some critical questions about the

blood tests after it answers the questions the system concludes

concerning the type of the blood disease.

The necessity of building such type of systems comes from

the remarkably increased number of patients of blood disease

and other diseases such as cancer during the recent years,

especially in Iraq due to the last decade’s wars [16]. The

determination of the type of the blood disease at an early

phase is important for the doctors for the treatment of these

diseases and to save the lives of the patients, the usable of the

system give chance to normal users to use the system without

doctor visiting.

The information’s that used in software development for

blood disease expert system are acquired from Kirkuk

General Hospital and the system are tested by the students of

Kirkuk University School of Medicine and Department of

Hematology in Kirkuk General Hospital and gave the

expected results.

The rules and parameters are determined by a predefined

knowledge base and utilized in the expert system called,

Software development for blood disease expert system

prepared by the software EXSYS CORVID. This expert

system may be used in determining the blood disease and can

be used in hospitals and universities by the doctors and

medical students. BDES superiority to the previous systems in

the usability, time saving and the number of diseases that

diagnosis by it with high performance. BDES can conclude

the result in one minute and easy to use, in the other hand, the

MYCIN cannot use by the normal user in real world because

it’s difficulty and it’s conclude the result within 20 minutes.

In addition to this study, the questions may be more

detailed and increase the number of diseases to develop the

abilities of the expert system.

ACKNOWLEDGMENT

I also place on record, my sence gratitude on and all, who

directly or indirectly, have lemt their hand in this venture.

REFERENCES

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182

Ahmad Mozaffer Karim was born in Kirkuk, Iraq on

April 5, 1987. In 2009, he received a B.Sc degree in

software engineering techniques from Kirkuk College of

Technology, Kirkuk, Iraq. He obtained an M.Sc degree

in computer engineering from Çankaya University

Ankara, Turkey, in 2012. He is currently a Ph.D student

at Graduate School of Natural Sciences, Yıldırım

Beyazıt University, Ankara Turkey. Also he is a systems analysis specialist

at CBKSOFT Ankara, Turkey. His research interests include multi-objective

optimization, expert systems, knowledge engineering, and artificial neural

networks .

Fatih Vehbi Çelebi was born in Kahramanmaras,

Turkey in 1963. He received his B.Sc. and M.Sc.

degrees in electrical and electronics engineering from

Middle East Technical University (METU) and

Gaziantep University Turkey, in 1988 and 1995,

respectively. He earned his Ph.D degree in

electronics/computer engineering from Erciyes

University in 2002. His research interests include fuzzy

logic, semiconductor lasers, and artificial neural networks. He is currently a

full professor and chairman of computer engineering at Yıldırım Beyazıt

University.

Adnan Saher Mohammed was born in Kirkuk, Iraq on

October 16, 1977. In 1999, he received a B.Sc degree in

computer engineering technology from College of

Technology, Mosul, Iraq. He obtained an M.Sc degree in

communication and computer network engineering from

UITEN University, Kuala Lampur, Malasyia, in 2012.

He is currently a Ph.D student at graduate school of

natural sciences, Yıldırım Beyazıt University, Ankara

Turkey. Also he is Computer Engineer in the Engineering Dept. Salahuddin

province. His research interests include computer network, computer

algorithms, and expert systems.


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Software Development for Blood Disease Expert System

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