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Computer Science and Information Systems 0(0):1–7 DOI: N/A

Expert System For Diagnose Stomach Disease Using Fuzzy Logic And Certainty Factor Method

I Ketut Gede Darma Putra, Ni Made Ika Marini, Ida Bagus Gede Dananjaya

Departement of Information Technology, Faculty of Engineering, Udayana University, Badung, Indonesia

[email protected], [email protected]

Abstract. Expert system is a decision maker system which work like a real expert and used to allow users to find out quick and accurate information. In the medical, quick and accurate decision is necessary to guarantee the safety of a patient's life. Expert System for diagnose stomach disease is created to diagnose ten types of disease in the stomach . The system is created with the aim to facilitate the doctor to make a decisions and to solve the problems such as the limited ability of the patient to pay a health expert diseases. Expert System was built using fuzzy logic combined with the certainty factor method. The combination of fuzzy logic and certainty factor in the study resulted an expert system that has an average response rate 75.25% corresponding percentage and the answer is very appropriate to have a percentage 25.5%. The average yield rate of the overall system response has been concluded in accordance with the expert knowledge.

Keywords: expert system; stomach disease; certainty factor; fuzzy logic.

1. Introduction

The basis of an expert system is how to move the knowledge possessed by an expert into a computer and how to infer or make decisions based on that knowledge. Storing the knowledge into computer needs a database of knowledge (Knowledge Base) that is database modeling determined in advance. Expert systems are used by doctors to help with evidences that are hard to diagnose and to suggest preventive measures or measures self care where even human experts have difficulty [10].

Expert System for Diagnose the Stomach Desease Using Fuzzy Logic and Certainty Factor is created to solve some problem like facilitate the doctor to make a decisions and to solve the problems such as the limited ability of the patient to pay a health expert diseases.The general description of the system can be seen at figure 1. The figure shown there are two types of area, consultation area and development area. Development area is the scope for the expert to input new knowledge to the database at expert system, so the system can be work better. The second scope is consultation area, this area is use by the patient to consultating with the expert system. First, the user will be given some question that come from the database and the user will give the answer to the system. The answer that given by user will be calculate using fuzzy logic and certainty factor.

Expert System For Diagnose Stomach Disease 2

And than the conclusion will be given to the user whether the user get stomach desease or not.

Fig. 1. General description

Expert system for diagnose the desease research is already been done by some people. For the example is Ali and Mehdi Adeli Neshat. They develop expert systems for diagnosis of heart disease by the method of Fuzzy Logic. The system has 13 input fields and one output field. Input fields are chest pain type, blood pressure, cholesterol, resting blood sugar, maximum heart rate, resting electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan, sex and age. The system is designed in matlab. The results obtained from the system are compared with the data in upon database and observed results of designed system are 94% correct [1].

Emmanuil Marakakis Kostas Vassilakis, Emmanuil Kalivianakis, Sifis Micheloyiannis develop the expert system for epylepsy with uncertainty. They created an expert system called HIPPOCRAT-EES for diagnose epylepsy. The system is created based of certainty factor.and have 83,3% of success rate [2]. Expert System for Diagnose the Stomach Desease Using Fuzzy Logic and Certainty Factor is different from the research that done before. It use the combination of two method. That two method are fuzzy logic and certainty factor, so the result will be better than the other.

2. Methodology

The knowledge acquired from the literature and single expert, including the gender of the patient, the day when the symptoms experienced by patient, clinical symptoms, syndrome symptoms and symptoms of routine hematology[3]. Table 1 shown the knowledge Acquisition of Stomach Disease.


We would like to stress that the template should not be manipulated and that the guidelines regarding font sizes and format should be adhered to. This is to ensure that the end product is as homogeneous as possible.

N

o

Disease S

ymptom

1 Ulcer Abdomi

nal Pain

Bloated

Nausea

2 Apendix Nausea

Lower

Abdoinal Pain

Pain

when folding

right leg

3 Gallstone Abdomi

nal Pain

Bloated

Belch

4 Kidneystone Low

Back Pain

Yellow

Turbid Urine

Kidneys

tone history

5 Colera Nausea

Abdomi

nal Muscle


Spasms

Diarrhe

a

6 Constipation Abdomi

nal Fullness

Full

Anus

Dark

Feses

7 Ulcus Nausea

Belch

Vomitin

g Blood

8 Dyspepsia Bloated

Nausea

Hot

Abdominal

9 Hernia Lower

Abdoinal Pain

Increas

e Pain when

Holding

Something

Bumb

Intestine

1 Dysentery Nausea

High


0 Body

Temperature

Degidra

te

2.1. Table 1. Symptoms of stomach disease

2.2. Fuzzy Inferetion

Fuzzy Logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than the crisp membership of classical binary logic. Unlike two-valued Boolean logic, fuzzy logic is multi valued. Fuzzy logic is a logic that describes fuzziness. As fuzzy logic attempts to model human's sense of words, decision making and common sense, it is leading to more human intelligent machines[4]. Fuzzy logic allows items to be described as having a certain membership degree in a set. This allows a computer, which is normally constrained to 1 and 0, to delve into the continuous realm [5]. An important concept in fuzzy logic lies in the concept of linguistic variables, variables whose value are word or sentences in natural langguange. Any relation between two linguistic variables can be expressed in terms of fuzzy if-then[6]. Table 2 shown the classification of Human Body Temperature.

Table 2. Classification of human body temperature

Interval Fuzzy Scope

<36oC Very Low

35oC-37oC Low

36oC-38oC Normal

37.5oC-39.5oC High

>38.5oC Very High

Figure 2 show the curve of fuzzy scope that mapping the input points to the membership value that have the intervall between 0 and 1.


Fig. 2. Human body temperature scope curve

Here some Calculation of using Fuzzy Logic. There is a condition that the system ask the user How much the temperature of your body and the user answer 39.7, so the calculation using fuzzy logic is below.

µTemperature=High (39.7) = 39,7−39

1.0=0.7

1.0=0.70

µTemperature=VeryHigh (39.7) = 39,7−39

1.0=0.2

1.0=0.20

2.3. Certainty Factor

Certainty Factor is introduced by Shortliffe Buchanan in making. Certainty Factor (CF) is a clinic parameter value which is given by MYCIN to show the measurement of belief. Certainty Factor (CF) shows the measurement of certainty to a fact or rule[7]. The basic idea underlaying the method is that when representing knowledge as production rules of the form if e then hx fi, a measure of uncertainty x is assosiated with the hypothesis h, expressing the degree to which the observation of evidence e influences the confidences in h[8]. Certainty Factor (CF) shows the measurement of certainty to a fact or rule. The using of certainty factor is done for: [9] Determining the measurement of belief to the early fact which will be given by every user,Determining the measurement of belief to conclusion or decision which is obtained from the rule; experts determine this value to the rule,Determining the measurement of belief to facts and result which is obtained along the process of reasoning from the result of the rule execution,Adjusting the measurement of belief to fact or result which is obtained from the different rule but producing the same conclusion.


Certainty factor can be calculate by this formula:

CF(x,y) = CF(x) * CF(y)

The calculation of both fuzzy value is like below.

CF Sequential A001222 = CF User * CF Expert

= 0.43 * 0.7 = 0.03

CF Sequential A001223 = CF User * CF Expert

= 0.43 * 0.8 = 0.034

Both sequential result then combined to get certainty value

with using CF Combination formula like this:

CF(x,y) = CF(x) + CF(y) – (CF(x) * CF(y))

The result of calculating using CF Combination is.

CF Combination = 0.03 + 0.34 – (0.3 * 0.34)

= 0.5

Result of calculating CF Combination is 0.5. Value of 0.5 means

the certainty value of user have high body temperature is 0.5 or 50%.

3. Results and Discussion

This is a view of application Expert System For Diagnose Stomach Disease Using Fuzzy Logic And Certainty Factor Method.


Fig. 3. Display system

Figure 3 displays one of many questions on this application.Users will be given five choice answers with Incorrect (0), Less Correctly (0.25), Almost True (0.5), Quite Qorrectly (0.75), and Very True (1). After user answer all questions, the values will be calculated using Fuzzy Logic Method and Certainty Factor Method.

Fig. 4. System test results

Figure 4 show the final result of application Expert System For Diagnose Stomach Disease Using Fuzzy Logic And Certainty Factor Method. The final results available


from the results of user answers that collected and then sought the value. After that the value will be calculated using Fuzzy Logic And Certainty Factor Method.

This is an example of sample test system using the data with level of response in most major limit. The system test is performed with input the answers level of confidence "Very True" for all symptoms questions. Illustration of the third phase of system testing are summarized in form of tables 3.

Table 3. The trial results to highest limit point

o

Quetions

Answer

ot

ru

e

es

s

ru

e

l

m

o

s

t

r

u

e

no

ug

h

ru

e

er

y

Tr

ue

How many

your body

temperature right

now?

36O

Do you feel

pain around your

stomach?

Do you feel

your stomach is

bloated?


Do you feel

nauseous (want to

vomit)?

Try to press

the lower part of your

stomach, do you feel

excruciating pain?

Try to fold

your right leg, do you

feel excruciating pain?

Do you often

do belching?

Do you feel

pain in the waist part?

Is your urine

yellow turbid?

0

Does your

family have a history

of kidney stones

disease?

1

Do you feel

convulsions around

the stomach muscle?

2

Does your

stomach feel fullness?


3

Do you feel full

in your anus?

4

Is your stool

color becomes dark?

5

Is your stool

turns greenish color

and mixed with blood?

6

When

vomiting, is you vomit

mixed with blood?

7

Does your

stomach feel

excruciating heat?

8

Do you feel

pain in the groin?

9

Is the lower

part of your stomach

gets worse when you

try to lift up the heavy

weights?

0

Do you often

feel thirsty

(dehydration)?

1

Do you have

diarrhea?


Diagnosis results

system

Ulcer

disease

1

00%

Appen

dix disease

1

00%

Gallst

ones disease

1

00%

Kidne

y stones

disease

1

00%

Chole

ra disease

1

00%

Conti

pation disease

1

00%

Ulcers

disease

1

00%

Dyspe

psia disease

1

00%

Herni

a disease

1

00%

Dysen

tery disease

1

00%

Expert questionnaire done to three experts. The weight of questions in the questionnaire to the experts are more emphasis on data consistency between the disease and the results of consultation. Expert use the system by trying to do consulting in various cases. Test results of system by an expert as shown in the table 4.

Table 4. Expert's valuation result to system suitability

Quetions Answer


o

D A

A V

A

Disease data in

the system % % %

6

7

%

3

3

%

Symptom data

in the system % % %

6

7

%

3

3

%

Diagnosis

results that produced

by the system already

appropriate with an

expert diagnosis results

% % %

1

0

0

%

0

Disease

solution features % % %

6

7

%

3

3

%

Average

% % %

7

5

,

2

5

%

2

4

,

7

5

%

VD = Very Disagree

D = Disagree

LA = Less Agree


A = Agree

VA = Very Agree

Table 4 shows the percentage of each question to the experts about the suitability of data and the results of consultation system. And then search the avarage of results from each quetion based on the rate of response. The Average of results are summarized in a graph in Figure 5.

Figure 5 Appropriate graph system from the expert

Graph in Figure 5 shows the spread of data at each level of the answers given by experts. Graph suitability system by an expert that committed to the three experts have resulted the conclusions :

Suitability of aspect disease data are getting assessment at two

levels answer, there are appropriate and very appropriate. Appropriate

answer have percentage as much as 67%. Very appropriate answer

have percentage as much as 33%.

Suitability aspects of symptoms get ratings on two level

answer, there are appropriate and very appropriate. Appropriate




Suitability aspects of consultation result get appropriate rate

from three experts.

Suitability aspects of disease tips feature get rate at two levels

answer, there are appropriate and very appropriate. Appropriate



The overall average of system performance get valuation on two levels answer there are appropriate and very appropriate. Appropriate answer have percentage as much as 75,25%. Very appropriate answer have percentage as much as 25,5%. The average from answer level concludes that overall of system is already appropriate with the expert knowledge.

4. Conclusion

Expert system is created using Certainly Factor Methode and Fuzzy Logic to overcome uncertainty factor from user's answer. The overall average of system performance get valuation on two levels answer there are appropriate and very appropriate. Appropriate answer have pefrcentage as much as 75,25%. Very appropriate answer have percentage as much as 25,5%. Overall the system result already appropriate with the expert knowledge. The average of overall system performance from user get valuation to three level answer there are enough, good, and very good. Enough answer have percentage as much as 22%. Good answer have percentage as much as 47,7%. Very good answer have percentage as much as 30,3%. The average result from level answer conclude overall system have good valuation.

5. References

1. Ali, Adeli. & Mehdi, Neshat., A Fuzzy Expert System for Heart Disease Diagnosis, Proceedings of the International MultiConference of Engineers and Computer Scientists, (2010)

2. Marakakis, at al., Expert System for Epilepsy with Uncertainty, ICGST, (2005)3. Dharma, Putra. & Manik, Prihatini., Fuzzy Expert System for Tropical Infectious Disease

by Certainty Factor, TELKOMNIKA, (2012)


4. D. W. Patterson, Introduction to Artificial Intelligence and Expert Systems, Prentice-Hall Inc., Englewood Cliffs, N.J, USA,(1990)

5. J.M. Mendel, Fuzzy Logic Systems for Engineering: A Tutorial, Proceedings of the IEEE, vol. 83, n°3, March 1995, pp. 345-377.

6. Bih, Josep., Paradigm Shift-An Introduction to Fuzzy Logic, Computer Information Systems, University of North Texas, T.X, USA, (2006)

7. Setyarini, at al., The Analysis of Comparison of Expert System of Diagnosing Dog Disease by Certainty Factor and Dempster-Shafer Method, ICJSI International Journal of Computer Science Issues, (2013)

8. Lucas, P.J.F., Certainty-Factor-Like Structures in Bayesian Belief Networks, Department of Computing Science, university of Aberdeen, Aberdeen, UK,(2000)

9. Smith Barbara S., uncertainty Reasoning and Representation: A Comparison of Several Alternative Approaches, Master Thesis, Graduate Program, Rochester Institute of Technology Department of Computer Science (1990)

10. Coppin, Ben., Artificial Intelligence Illiminated. Canada Jones and Bartlett Publishers, Inc, (2004)

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