PENULISAN ILMIAH

Web-Based Expert System To Diagnose Heart And Lung Diseases Using Fuzzy Logic

And Certainty Factor

(International Journal of Knowledge-Based and Intelligent Engineering Systems)

(Website: http://www.iospress.nl/journal/international-journal-of-knowledge-based-and-intelligent-engineering-systems/ )

Oleh:

Erna Yulianti (1104505008 )

RR.Siti Sarah Wulan A.Y.U (1104505044)

Tantony Hardiwinata (1104505047)

Ni Kadek Ayu Anggraeni (1104505050)

Ni Putu Sri Merta Suryani (1104505060)

AA.Primaningrat Gita Puspita (1104505066 )

JURUSAN TEKNOLOGI INFORMASI

FAKULTAS TEKNIK UNIVERSITAS UDAYANA

2015

Web-based expert system to diagnose heart and lung diseases using fuzzy logic and certainty factor

I Ketut Gede Darma Putraa*, I Putu Agung Bayupatib and Ni Kadek Ayu Anggraenic

abcInformation Technology, Faculty of Engineering, Udayana University, Bali, Indonesiaaikgdarmaputra@gmail.com, b bayupati@ee.unud.ac.id c ayuanggraeni122@gmail.com,

Abstract.The aim of this study is to design a web-based expert system for diagnosis of heart and lung diseases using the combination of fuzzy logic and certainty factor methods. This system uses Sugeno inference method and forward-backward chaining as the reasoning approach method. The developed expert system has 5 fuzzy input variables namely body temperature, pulse rate, systolic blood pressure, body weight, and blood sugar. The results obtained from designed expert system were compared with medical expert diagnosis and has similarity at 94.61%.

Keywords: Expert system, heart and lung diseases, fuzzy logic, certainty factor

1. Introduction

Expert system allows the communication between doctor and patient can be done without limitation of distance and time. This system able to provide solutions for some problems which contain elements of uncertainty to solve the similarity between the symptoms of one to other diseases. Capabilities provided by it, is a solution to overcome the problems that often occur in the process of patient management, especially for people who live in the areas with minimum specialist.Cases in internal diseases such as

cardiovascular and pulmonary will be greatly assisted by the expert system. The method for diagnosis heart’s illnessare using certainty factor method. Cardiovascular illneswas classified into 25 types, left and right side of the heart failure related to semantic nerwork [5][10]. Development of expert systems for diagnosis of heart disease using Fuzzy Logic method have attribute 11 input variables and one output variable. The inference method is Mamdani method and defuzifikasi method used is the centroid method [1].

Some intelligent techniques as the Neural Networks, the Fuzzy Logic, and Certainty Factor [1-5] have been used to create expert system for internal desease and infection. But,there is no expert system for detection more than one

concentration of internal desease using combination of 2 or moremethods.

Developing expert system can be done using single method or more. But, to make the result strongly sure, it should be done by combining two or more methods.

Method of developing expert systems for diagnosis disease can be made by combining the method of Neural Network and Certainty Factor. Models are constructed consisting of three phases, each phase using a single network for the learning process [6]. Development of expert systems for diagnosis hepatitis B by comparing the Fuzzy Inference System (FIS) and AdaptiveNeuro-Fuzzy Inference System (ANFIS) can be done with inference method used in FIS, Mamdani, while the ANFIS Sugeno [2].

Based on some studies, the paperis concernedto use two methods. These can be used and resolve problems that occur, either by using the Fuzzy Logic method or by using Certainty Factor, both fully can help resolve issues related to expert systems, of excess and disadvantages of each method. Therefore, the authors are interested in developing an expert system for diagnosing cardovascular and pulmonary desease using Fuzzy Logic and Certainty Factor.

The first method, Fuzzy Logic method will be used to deal with the uncertainty of the symptoms experienced by patients and

methods.Then the second method, Certainty factors will be used to address the inability of an expert in defining the relationship between the symptoms of the disease with certainty. The implementation was trough seven stage, they are data collection phase of the diseases, fformulation stage of the diseases and its symptoms, stage of the rule (the expert system rule), database design phase, expert system interface design, implementation of the design phase into the expert system, and testing phase.

This expert system also provides improved knowledge and explanations, which the expert can add new knowledge to a disease or amend existing knowledge on the disease, so that the system will remain accurate and up to date.

2. Research method

Types of diseases that were made as the research objects are pneumonia, pulmonary tuberculosis, asthma, COPD, lung cancer, chronic heart failure, coronary heart disease, hypertensive heart, heart valves, and congenital heart diseases. The output of this expert system is the belief of illness.

Diseases symptoms that included in this research are divided into two modeling, fuzzy modeling and crisp modeling. Fuzzy modeling has 5 input variables (body temperature, pulse rate, systolic blood pressure, body weight, and blood sugar), meanwhile crisp modeling consists of 47 symptoms. Each crisp symptom are modeled into two sets of value (Yes or No). Patient are given multiple choice to define a crisp symptom, that is “No”, “A bit of”, “Enough”, “Very”, and “Very High” with its own value

2.1. Fuzzy expert system

Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic[8]. In other words, a fuzzy expert system is an expert system that uses fuzzy logic instead of Boolean logic[9]. The differences between these two logic are depends on the value, while Boolean logic only recognized True/False value, the fuzzy logic have a truth value that range between 0 to 1.

The process of the system begins with knowledge acquisition of symptoms for each disease. The next process is knowledge presentation of the facts that have been obtained

from the previous step and determine whether the symptoms are suitable for crisp or fuzzy set. Crisp symptoms are modeled in two sets, while fuzzy symptoms are processed further to find the degree of membership.

The next process is fuzzification which is the process of changing a real crisp value (true or false) into a fuzzy value. The first step of fuzzification is to construct rules by using the input parameters of fuzzy expert system. Certainty factor then used to draw conclusion of rules that applied in certain events based on facts or symptoms. The aim of certainty factor is to give a value of trust which is useful for determining the confidence level on the relationship between symptoms that exist in fuzzy rule.

After fuzzification has been conducted, the next step is the implication and composition process that used in consultation environment between user and expert system. The final process is defuzzification that translates the output from inference engine into crisp output. The crisp output then offered to users as the final conclusion of facts or symptoms that given.In addition, knowledge improvement is a process to update data in knowledge base.

2.2. Input variables classification

The first step of fuzzy expert system processing is determining the input variable. Input variable that used are body temperature, pulse rate, systolic blood pressure, body weight and blood sugar.

a. Body temperature.This input variable has divided into 5 fuzzy

sets: “Very Low”, “Low”, “Normal”, “High” and “Very High”. Membership function of “Low”, “Normal” and “High” sets are triangular, while membership function of “Very Low” and “Very High” sets are trapezoid. These fuzzy sets will be shown in Table 1. Membership functions of body temperature field will be shown in Fig. 1.

Table 1. Classification of Body Temperature

Input Variable Range Fuzzy Sets

Body Temperature

<360 C Very Low350C – 370C Low360C – 380C Normal37.50C – 39.50C

High

>38.50C Very High

Fig.1. Membership functions of body temperature

Membership functions of body temperature:

μVeryLow (x ) ={36-x1.0

, 35≤x≤36

1, x≤35

μLow (x ) = {x-351.0

, 35≤x≤36

1, x=3637-x1.0

, 36≤x≤37

μNormal ( x ) ={x-361.0

, 36≤x≤37

1, x=3738-x1.0

, 37≤x≤38

μHigh ( x ) ={x-37.51.0

, 37.5≤x≤38.5

1, x=38.539.5-x1.0

, 38.5≤x≤39.5

b. Pulse rate.Pulse rate variable has 5 fuzzy sets: “Very

Low”, “Low”, “Normal”, “High” and “Very High”. Membership function of “Low”, “Normal” and “High” sets are triangular, while membership function of “Very Low” and “Very High” sets are trapezoid. These fuzzy sets will be shown in Table 2. Membership functions of pulse rate field will be shown in Fig.2.

Table 2. Classification of pulse rate

Input Variable Interval

Fuzzy Set

Pulse Rate 0 – 50 Very Low

30 – 70 Low50 – 100 Normal90 – 140 High>120 Very High

Fig. 2. Membership functions of pulse rate

Membership functions of pulse rate:

μVeryLow (x ) ={50-x20

, 30≤x≤50

1, x≤30

μLow (x ) = {x-3020

, 30≤x≤50

1, x=5070-x20

, 50≤x≤70

μNormal ( x ) ={x-5025

, 50≤x≤75

1, x=75100-x25

, 75≤x≤100

μHigh ( x ) ={x-9025

, 90≤x≤115

1, x=115115-x25

, 115≤x≤140

μVeryHigh ( x )= {x-12020

, 120≤x≤140

1, x≥140

c. Systolic blood pressure.This input variable has divided to 5 fuzzy

sets: “Very Low”, “Low”, “Normal”, “High” and “Very High”. Membership function of “Low”, “Normal” and “High” sets are triangular, while membership function of “Very Low” and “Very High” sets are trapezoid. These fuzzy sets will be shown in Table 3. Membership functions of

systolic blood pressure field will be shown in Fig.3.

Table 3. Classification of systolic blood pressure

Input Variable Interval Fuzzy Set

Systolic Blood Pressure

<50 Very Low

40 – 100 Low90 – 140 Normal120 – 160

High

>150 Very High

Fig.3. Membership functions of systolic blood pressure

Membership functions of systolic blood pressure:

μVeryLow (x ) ={1, x≤4050-x10

, 40≤x≤50

μLow (x ) = {x-4030

, 40≤x≤70

1, x=70100-x30

, 70≤x≤100

μNormal ( x ) = {x-9025

, 70≤x≤115

1, x=115115-x25

, 115≤x≤140

μHigh ( x ) ={x-12020

, 120≤x≤140

1, x=140160-x20

, 140≤x≤160

μVeryHigh ( x )= {x-14010

, 140≤x≤150

1, x≥150

d. Body weight.Body weight variable is divided into two

categories based on the gender, adult female and adult male. Each category has 5 fuzzy sets (“Very Low”, “Low”, “Normal”, “High” and “Very High”). Membership function of “Low”, “Normal” and “High” sets are triangular, while membership function of “Very Low” and “Very High” sets are trapezoid. These fuzzy sets will be shown in Table 4. Membership functions of female body weight field will be shown in Fig. 4.

Table 4. Classification of adult female body weight

Input Variable Interval Fuzzy SetAdult Female Body Weight

<40 Very Low35 – 47 Low45 – 162 Normal60 – 67 High>65 Very High

Fig.4. Membership functions of adult female body weight

Membership functions of adult female body weight:

μVeryLow (x ) ={1, x≤4035-x5

, 35≤x≤50

μLow (x ) = {x-357

, 35≤x≤40

1, x=4047-x7

, 40≤x≤47

μNormal ( x ) ={x-4515

, 45≤x≤47

1, x=4762-x15

, 47≤x≤62

μHigh ( x ) ={x-605

, 60≤x≤62

1, x=6267-x5

, 62≤x≤67

μVeryHigh ( x )= {x-652

, 165≤x≤67

1, x≥67

Fuzzy sets for male body weight input field will be shown in Table 5. Membership functions of male body weight field will be shown in Fig.5.

Table 5. Classification of adult male body weight

Input Variable

Interval Fuzzy Set

Adult Male Body Weight

<40 Very Low35 – 47 Low45 – 162 Normal60 – 67 High>65 Very High

Fig.5. Membership functions of adult male body weight variable

Membership functions of adult male body weight:

μVeryLow (x ) ={1, x≤4245-x3

, 42≤x≤45

μLow (x ) = {x-4215

, 42≤x≤45

1, x=4560-x15

, 45≤x≤60

μNormal ( x ) ={x-533

, 53≤x≤60

1, x=6063-x3

, 60≤x≤63

μHigh ( x ) ={x-6022

, 60≤x≤63

1, x=6385-x22

, 63≤x≤85

μVeryHigh ( x )= {x-805

, 80≤x≤85

1, x≥85

e. Blood sugar.Blood sugar variable divided into 5 different

fuzzy sets: “Very Low”, “Low”, “Normal”, “High” and “Very High”. The membership function of “Very Low” and “Very High” sets are trapezoidal while “Low”, “Normal” and “High” sets are triangular. Table 6 below depict the fuzzy set classification for this variable. Figure 6 is the graphical representation of the membership function curve.

Table 6. Classification of blood sugar

Input Variable Interval Fuzzy Set

Blood Sugar < 70 Very Low

60 – 130 Low

120 – 180 Normal

170 – 240 High

> 230 Very High

Fig6. Membership functions of blood sugar

Membership functions of blood sugar:

μVeryLow (x ) ={1, x≤6070-x10

, 60≤x≤70

μLow (x ) = {x-6060

, 60≤x≤70

1, x=70130-x60

, 70≤x≤130

μNormal ( x ) ={x-12050

, 120≤x≤130

1, x=130180-x50

, 130≤x≤180

μHigh ( x ) ={x-17060

, 170≤x≤180

1, x=180240-x60

, 180≤x≤240

μVeryHigh ( x )= {x-23010

, 230≤x≤240

1, x≥240

3. Experiment

System testing is done to ensure that the generated diagnosis has the same similarity with the medical expert analysis. This experiment was conducted using web interface that programmed with PHP and HTML programming.To obtain satisfactory results of the study, the addition of the knowledge base were by the system administrator. Consultations were carried out among patients with expert system by answering some questions about symptoms of diseases.

The system consists of several processes such as disease process data input, input process symptoms, the input process relationship of symptoms and disease, input process fuzzy answer, input process rules, and minimum percentage configurations.

Diagnosis results will be described using calculation of the consultation process with the

order of questions as shown in the pictures Fig. 7.

Fig. 7. Gender question

Fig. 7. is the consultation form related to the gender of the patient. Based on the answers given patient, for example is a male, then the system will search for the question of the symptoms that affected male gender.

Fig. 8. Weight symptoms

Fig. 8. is the consultation form for the male body weight symptoms input field. The system will calculate the degree of membership of the input response of the patient.

µWeight = High(70) = 0.80

After patient answered questions, the system will proceed to the next question shown in Fig.9.

Fig. 9. Symptoms of shortness of breath

Tin Fig. 9. it is shown that the patient chooses the answer with the value of 8.38. The system will calculate the degree of membership.

µBreathless = VeryHigh(8.38) = 0.38

µShortnessOfBreath= High(8.38) = 0.41

After filling in the question, the system will proceed to the next question as shown in Fig. 10.

Fig. 10. Symptoms of active smokers

Fig. 10. is the consultation form related to symptoms of active smokers. The patient chooses the answer with the value of 7.42. The system will calculate the degree of membership.

µSmokers = High(7.42) = 0.94

After filling all the questions available, the system will perform the inference process to generate the result of consultation using percentage of belief in each disease.

The next process is the implication process by finding the rules base on reasoning approach that store in system database. Rules query is done by using production system (IF…THEN). Result that obtained from overlapping rules then compared with each other, the system will select the minimum value. Fig. 11. is the final result of fuzzy expert system.

Fig. 11. Final result of system

System uses verification method to test the final result as shown in Fig. 11. The test will look for the similarities of diagnosis between medical expert and developed expert system. Testing is done directly by the expert specializing in internal medicine.

Test carried out on 10 different cases, based on the test results of each case, the average calculation diagnosis made by physicians and system for each test case with the following formula:

Average results of the doctor's diagnosis=∑i=1

N

doctor diagnosisi

N

Average results of diagnosis system =∑i=1

N

diagnosis system i

N

Differential diagnosis made by the expert and system for each test case is calculated with the following formula:

Difference=¿diagnosis systemi−doctor diagnosisi∨¿

doctor diagnosisi

x100 %¿

The result of this calculation can be seen in Table 7.

Table 7. Difference in the doctor's diagnosis and systemNo. Expert (%) Fuzzy Expert

System (%)Gap of

Diagnosis (%)1 45 48 6.672 64 60 6.253 56 58 3.574 43 45 4.655 55 50 9.096 72 76 5.567 76 70 7.898 42 44 4.769 54 57 5.5610 0 0 0

Average of Gap of Analysis between Expert and Fuzzy Expert System (%)

5.39

The difference between the results of the diagnosis made by the doctor and the system against 10 cases were tested at 5.39%. This suggests that the expert system has developed a level of similarity with the real experts at 94.61%.

4. Conclusion

The purpose of this research is to build a web-based expert system for the diagnosis of diseases that related to human's heart and lung organ. This system developed by using the combination of certainty factor and fuzzy logic method. The developed system is expected to facilitate the patient or medical doctor in health consultation regarding symptoms that may lead to both diseases. The system provides output of diagnosis expressed in percentage of belief. Test result revealed that system result has similarity at 94.61% compared to medical expert diagnosis.

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