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Case Report - Volume 2 - Issue 2

Feature Selection Based Fuzzy Expert System for Efficient Diagnosis of Coronary Artery Disease

Y Atomsa1; LJ Muhammad1*; FS Ishaq 3and Yusuf Abdullahi2

1Computer Science Department, Federal University of Kashere, Gombe State, Nigeria.
2Computer Science Department, Federal University of Kashere, Gombe State, Nigeria.
3Computer Science Department, Federal University of Kashere, Gombe State, Nigeria.
4Biological Sciences Department, Federal University of Kashere, Gombe State, Nigeria.

Received Date : Feb 11, 2022
Accepted Date : Mar 04, 2022
Published Date: Mar 18, 2022
Copyright:© LJ Muhammad 2022

*Corresponding Author : LJ Muhammad, Computer Science Department, Federal University of Kashere, Gombe State, Nigeria.
Email: lawan.jibril@fukashere.edu.ng
DOI: Doi.org/10.55920/2771-019X/1103

Abstract

Coronary artery disease (CAD) is one of the most dangerous diseases which lead to sudden cardiac death. According to Word Health Organization, CAD is the number one killer in the developed world, with over 7.4 million deaths attributed it. Before, CAD is not common disease in Nigeria, however, is at this moment gaining much popularity in the country following the rising number of health issues related to CAD diseases, including higher death rate, which is mostly due to lack of proper awareness among the common people. The diagnosis of CAD is very expensive and time consuming which made computer scientists to use artificial techniques such as expert system to diagnose CAD’s patients. Therefore, in this work, fuzzy based expert system for efficient diagnosis of coronary artery disease has been developed, implemented and evaluated. Hence, the system has archived 90.08% overall accuracy which is very excellent, thus the accuracy determines the proportion of the total number of predictions that were correct. At the same time, the system has 91.30% accuracy to classify of normal patients correctly by the system (specificity) and 90.24% accuracy to classify abnormal patients correctly by the system (sensitivity). This showed that, the system performed efficiently and excellently to diagnose CAD.

Keyword: CAD; Artificial Intelligence; Expert System; Fuzzy Logic; dataset; diagnosis.

Introduction

Coronary Artery Disease (CAD) is one of the deadliest diseases in the world. It has been estimated that nearly one half of all middle-aged men and one third of middle-aged women in the United States have been affected with the CAD disease [46, 50]. In the developed countries CAD is one of the number one killers with over 7.4 million deaths attributed to [29, 48]. It has been estimated that, CAD is one in every seven deaths in the United States is due to heart disease. CAD is the primary cause of death in women, taking more lives than all cancers combined The proportion of deaths in the United States that are due to CAD has been decreasing slowly but continuously over the past half century. Nonetheless, CAD remains the single most common cause of death in the United States, according [16, 19, 46]. In Nigeria, CAD is at the moment gaining much popularity following the rising number of health issues related to the disease, including higher death rate, which is mostly due to lack of proper awareness among the common people. According to the latest WHO data published in May 2014 Coronary Artery Disease Deaths in Nigeria reached 53,836 or 2.82% of total deaths. The rising figures of health issues (cases) and mortality rate related to CAD in Nigeria recently are alarming [2, 7, 26-27]. Research outputs have suggested a rather increasing occurrence of instances of the CAD in the Nigerian community, which however, the populace seems not to be well informed and/or alarmed about it, and also, given the health emergencies response level of Nigeria, the Nigerian health systems do not seem very readily capable to deal with the menace of CAD in both an immediate and a continuous strategy to lower and/or overcome its adverse effects on the population [26-27]. However, the major problem in traditional method of medical diagnosis is inadequate guarantee of precision and accuracy. There are huge data management tools available within health care systems, but analysis tools are not sufficient to discover hidden relationships amongst the data. Most of medical information is vague, imprecise and uncertain [8]. Extracting correct information from this data is considered an art [16, 12-13]. It can be said to be an art because it is complicated by many factors and its solution involves literally all of a human's abilities including technical expertise and intuition. Hence, it becomes necessarily essential to leverage computing technologies that support analytical processing of this data to extract hidden correlations and intelligences within the data in order to improve accuracy and precision of medical diagnosis [15].

Expert systems have been specifically applied in a variety of life sciences support systems development, ranging from storage and retrieval of medical records, diagnostics, up to expert knowledge/decision support systems [3-5, 9]. Expert system defined as an intelligence system that extracted its knowledge using appropriate technique with the perception of human expert, to solve the problems or make decision as human being does [10, 44]. Expert System is defined as an intelligence system which uses extracted knowledge from past domain expert decision making reasoning in form of rules to solve problems that ordinarily require human expertise for their solution, and has the capability to update its rule-base as new knowledge is discovered. There are many application areas of expert system such as medicine, education, agriculture, oil and gas, environment, law, manufacturing, telecommunication and power systems etc. [11]. This research work aims to develop a fuzzy based expert system for supporting and completing human expertise in the diagnosis of Coronary Artery Disease. The focus is leveraging computing technology systems for the efficient, precise and re-accessible diagnosis procedure for the Coronary Artery Disease.

 Related Work

In this study research articles and conference papers published by reputable pub­lishers that employed expert system for diagnosis of coronary artery disease were searched and reviewed in this section. In work [47] an expert system for the diagnosis of the level of coronary heart disease by taking into account the problem of data imbalance developed. In the study of [14], a fuzzy soft sets expert system to predict patients suffer coronary artery disease was developed. The research was a pioneering approach in applying fuzzy soft sets to a medical diagnosis problem in the form of predicting patients who may be suffering from coronary artery disease. In study of [39], a web based Fuzzy Logic-based Expert System for the diagnosis of heart failure disease was developed. An evolutionary fuzzy expert system is proposed for the diagnosis of the Coronary Artery Disease (CAD) based on Cleveland clinic foundation datasets for heart diseases in the study of [49]. In the study of [1], patients with coronary artery disease were identified and classified through the neuro-fuzzy network with the capacity of automatically extracting fuzzy rules. Fuzzy expert system was implemented using facilities and functions of MATLAB software (7.12.0 version). A fuzzy expert system for diagnosis of coronary artery disease by a non-invasive procedure was implemented in the study of [33]. The adaptive neuro fuzzy inference system and Advanced fuzzy resolution mechanism has been proposed in the study of [17] to diagnose the heart disease. The work of [38] has developed a computer intelligent based approach for the diagnosis of heart diseases. A fuzzy rule-based system which concentrated only on accuracy and interpretability of the system was proposed by Ref [36] and system that provided a heart disease patient with background for suitable diagnosis and treatment. Ref. [42] developed a weighted fuzzy rule-based Clinical Decision Support System (CDSS) for computer-aided diagnosis of the heart disease. The different data mining techniques such as neural networks, decision trees and naive bayes has been proposed in the work of [45] for the study of heart disease prediction system. A coronary artery disease fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm, has been proposed in the study of [18] for obtaining near rule set and membership function tuning. In the study of [43] screening system has been developed for the early detection of Coronary Artery Disease. In the study of [33], a fuzzy rule-based system was designed to serve as a decision support system for diagnosis Coronary heart disease. In the work [6] a Fuzzy Expert System for heart disease diagnosis using V.A. Medical Center, Long Beach and Cleveland Clinic Foundation database was designed and system is being designed with in Matlab software and it is viewed as an alternative for existing methods to distinguish of heart disease presence.

Methods and Materials

Dataset

The medical expert diagnostic dataset for coronary artery disease obtained at the Federal Teaching Hospital, Gombe State, Nigeria were prepared in the appropriate format with the helped of medical experts in the hospitals and only data instances of the dataset without missing values were considered and collected. Therefore, there are only one thousand two hundred and one data instances of the dataset without missing value. The dataset is labeled one with eighteen features including demographic, history and clinical features of the patient’s CAD. The feature of the dataset are age, sex, CAD family history, smoking, type of the chest pain, diabetes, glucose, hypertension, blood pressure, cholesterol, Hyperlipidemia, high density lipoprotein (HDL), Triglyceride, low density lipoprotein (LDL), Creatinine, BodyMass, HeartRate and Diagnosis (Table 1) shows the description of the dataset features.

Table 1: Description of the Dataset Features.

Feature Selection with Correlation Analysis

Correlation coefficient analysis was carried out on the dependent and independent features of the CAD Dataset [20]. Correlation coefficient is used to determine the strength relationship that exists between the dependent and independent features which can either be positive or negative. The r value is a set of infinite number between -1 to +1 which show the existing relationship either positive or negative between the dependent and independent features [22-24]. The feature can be evaluated by the equation (1) below:-

Where the Importance is the correlation coefficient between dependent feature set and independent feature and is the ranking criteria for evaluating the set of feature, (avg(corrfc)) is the average of the correlation between the dependent feature and the independent feature, avg(corrff) is the average of the correlation between feature set, and k is the number of features. Correlation coefficient analysis was carried out on the dependent features of the CAD dataset which include Age, Sex, Family History, Smoking, Chest Pain, Diabetes, Glucose, Hypertension, Blood Pressure, Cholesterol, Hyperlipidemia HDL, Triglyceride, LDL, Creatinine, Body Mass and Heart Rate and Diagnosis feature which is an independent features of the CAD Dataset. (Table 2) and shows the r value of dependent feature against the independent feature of the dataset while (Figure 1) shows entire the correlation coefficient analysis matrix of the dataset features.

Figure 1: The correlation coefficient analysis matrix of the dataset features.

Table 2: r value of the correlation coefficient analysis.

We remove the all independent attributes that have less than 0.50 positive correlation coefficient relationships with dependent attributes of the dataset as shown in (Table 3).

Table 3: Independent Attributes with more with 0.50 positive correlations and above.

Mining Dataset with selected features

Interpreting mined patterns: in this phase, the visualization the extracted hidden patterns or knowledge using C4.5 algorithm was generated and it is called decision tree. However, only independent attributes with more with 0.50 positive correlations and above were used to build the decision tree with C4.5 algorithm. Figure 2 has shown the visualization decision tree of the algorithm. C4.5 is one of the data mining classification algorithms and it is an extension of Quinlan’s earlier ID3 algorithm. C4.5 algorithm was proposed by Ross Quinlan in 1993 to overcome some of the limitations of ID3 algorithm [25, 28, 51]. One the limitation of ID3 algorithm overcomes by C4.5 is ID3 sensitivity to features with number of values [30, 41].

Figure 2: Decision tree of C4.5 using CAD Dataset.

The decision tree generated with C4.5 algorithm was converted or transformed into or crisp rules. Below are the corresponding crisp rules of generated from the decision tree in (Figure 2).

IF (HeartRate <99.5 mg/dl and BP <152.5 mg/dl and Hypertension =No and Diabates < 0.15 and Sex < 47 ) THEN Negative

  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension= Yes and Diabates < 0.15 and Sex > 47 ) THEN Positive
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension = Yesand Diabates <2.85 ) THEN Negative
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension = No and Diabates =>1.52 and Chest pain= non_anginal ) THEN Negative
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension =No and Diabates =>2.2 and Chest pain= asymt and BMI >=19) THEN Negative
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension =Yes and Diabates = Yes and Chest pain= asymt and BMI >=19 and Sex < 65) THEN Positive
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension >=152.2 and Diabates = Yes and Chest pain= atyp_angina and Glucose < 69.5) THEN Positive
  • IF (HeartRate <99.5 mg/dl and BP < 152.5 mg/dl and Hypertension >=152.2 and Diabates = Yes1 and Chest pain= atyp_angina and Glucose >= 69.5) THEN Positive
  • IF (HeartRate <=99.5 mg/dl and BP < 152.5 mg/dl and Hypertension >=152.2 and Diabates = No and Chest pain= atyp_angina and Glucose >= 69.5) THEN Negative
  • IF (HeartRate <=99.5 mg/dl and BP < 152.5 mg/dl and Hypertension = Yes and Diabates = Yes and Chest pain= typ_angina ) THEN Positive
  • IF (HeartRate < 99.5 mg/dl and BP < 152.5 mg/dl and Hypertension= Yes and Glucose >= 217.5 ) THEN Positive

Transformation of crisp set rules into fuzzy set of rules

In this phase, the crisp set rules selected using RST are converted into fuzzy set rules. the linguistic variable of each attribute are determined, membership function of those linguistic variable of each input and output are also determined, calculated and visualized using MATLAB. So each crisp value has be transformed or converted into fuzzy value as such, all the crisp set rules generated using C4.5 decision tree algorithm be transformed into the corresponding fuzzy set rules. Trapezoid membership function is adopted for all inputs while triangular membership is adopted for output. (Table 4) shows the input (variables), their ranges and Linguistic Terms.

Table 4: Input variables, their ranges and Linguistic Terms.

a) Sex: This input has two instances; either the patient is male or female. Male = 1 and Female = 0 Hence, there is no fuzziness or overlap for this input. The membership functions of the linguistic variables of the sex input is shown is (Figure 3).

Figure 3: Membership functions of the linguistic variables of sex.

Chest pain: This input has four Chest Pain types: Typical Angina, Atypical Angina, NonAngina, and Asymptomatic. One Patient can have only one type of Chest Pain at a time. To represent Chest Pain, 1 = Typical Angina, 2 = Atypical Angina, 3 = Non-Angina and 4 = Asymptomatic. Hence, there is no fuzziness or overlap for this input, thus there are crisp set of values because the patient patient has just one chest pain at a time and the membership functions of the linguistic variables of the chest pain input is shown is (Figure 4).

Figure 4: Membership functions of the linguistic variables of Chest Pain.

Diabetes: This input has two instances; either the patient is positive or negative. Positive = 1 and Negative = 0 Hence, there is fuzziness or overlap for this input. The membership functions of the linguistic variables of the diabetes input is shown is (Figure 5).

Figure 5: Membership functions of the linguistic variables of Diabetes.

Result of Diagnosis (Output): The output consists of four fuzzy sets and their linguistic variables are Healthy, Mild, Moderate and Severe. Each Linguistic variable has membership function associated with it. The membership functions of the linguistic variables of Diagnosis output is shown is (Figure 6).

Figure 6: Membership functions of the linguistic variables of Diagnosis.

Fuzzification

Moreover, after determining the linguistic variable of each attribute, converting crisp value into fuzzy values, the crisp set rules generated earlier were converted into fuzzy set of rules. Fuzzification is the process of transforming crisp values into grades of membership for linguistic terms of fuzzy sets. The membership function is used to associate a grade to each linguistic term. Below are some of the fuzzy rules.

  • IF (HR is Normal and BP is Normal and Glucose is Low and Diabetes is Yes and Sex is Female ) THEN Healthy
  • IF (HR is Normal and BP is Low and Glucose = Normal is High and Diabetes = Yes and Sex is Male ) THEN Mild
  • IF (HR is Low and BP is Normal and Glucose is Norma and Diabetes is Yes and Sex is Male ) THEN Moderate
  • IF (HR is Low and BP is Low and Glucose is Low and Diabetes is No and Sex is Female) THEN Severe
  • IF (HR is Normal and BP is Normal and Glucose is High and Diabete ) THEN Healthy
  • IF (HR is Low and BP is Low and Glucose is High and Diabetes is Yes) THEN Mild
  • IF (HR is Normal and BP is Normal and Glucose is High and Diabetes is No and Chest pain is non_anginal) THEN Healthy
  • IF (HR is Low and BP is Low and Glucose is High and Diabetes is Yes and Chest pain is non_anginal ) THEN Mild
  • IF (HR is Low and BP is Normal and Glucose is High and Diabetes is No and Chest pain is asymt and and BMI is Normal) THEN Healthy
  • IF (HR is Normal and BP is Low and Glucose is High and Diabetes is No and Chest pain is asymt and and BMI is High)THEN Mild
  • IF (HR is Low and BP is Normal and Glucose is High and Diabetes is No and Chest pain is asymt and and BMI is Normal and Sex is Female ) THEN Moderate

 

Knowledge inference or Knowledge reasoning

Knowledge inference or Knowledge reasoning: this involves application of logical rules to the knowledge to deduce new information. The inference engine draws conclusions from the replicated human expertise in the knowledge base of the expert system, which is the hallmark of the expert system [37, 40]. Mamdani inference technique is used to stimulate reasoning of expert physicians in field of diagnosis of coronary artery disease in this work.. The usage of Mamdani technique is shown in (Figure 7).

Figure 7: Usage of Mamdani technique.

Defuzzification

Defuzzification is the process of converting the output of inference engine (fuzzy values) into crisp values. As the name implies, defuzzification is the opposite operation of fuzzification. Since in the first procedure the crisp values of input variables are fuzzified into degree of membership with respect to fuzzy sets, the last procedure extracts a precise quantity out of the range of fuzzy set to the output variable [34-35]. The Defuzzifier technique employed in this work is Centroid. Centroid defuzzification returns the center of area under the curve [31]. Centroid Method (also called center of area or center of gravity) which is the most prevalent and physically appealing of all the defuzzification methods. It is adopted in this study. It is given by the algebraic expression below:-

Where z is the output variable, and ????????(????) is the membership function of the aggregated fuzzy set A with respect to z. the .

Fuzzy Based Expert System for Efficient Diagnosis of Coronary Artery Disease

Like any other Fuzzy Inference System, 4.6. Fuzzy Based Expert System for Efficient Diagnosis of Coronary Artery Diseasehas been implemented in MATLAB and it has five primary graphical user interfaces (GUIs) can all interact and exchange information to each other as shown in (Figure 14). Any of the interfaces can read and write both to the workspace and to the disk, (the read-only viewers can still exchange plots with the workspace and/or the disk). Like any fuzzy inference system, any or all of these five GUIs can be opened. (Figure 8) shown GUI of the expert system and other GUIs of the system include Membership Editor Viewer, Rule Editor Viewer, Rule Viewer and Surface Viewer. GUI of Membership Editor Viewer is shown in (Figure 9), GUI of Rule Editor Viewer is shown in (Figure 10), GUI of Rule Viewer is shown in (Figure 11 and Figure 12) has shown the GUI of Surface Viewer.

Figure 8: GUIs of Feature Selection Based Fuzzy Expert System for Efficient Diagnosis of Coronary Artery Disease.

Figure 9: GUI of Membership Editor Viewer.

Figure 10: GUI of Rule Editor Viewer.

Figure 11: GUI of Rule Viewer.

Figure 12: Surface Viewer.

Performance Evaluation of the Fuzzy Based Expert System

There are four classes of linguistics variable of the system output which include Healthy, Mild, Moderate, and Severe. Both Healthy, Mild patients are considered as normal patients and both Moderate and Severe are considered as abnormal (CAD) patients while evaluating the performance of the fuzzy expert system model. For the performance evaluation of the system were considered based on the following techniques:-

  1. True positive (TP): It denotes the number of abnormal patients correctly classified by the framework.
  2. True negative (TN): It denotes the number of normal patients correctly classified by the framework.
  • False positive (FP): It denotes the number of normal patients wrongly classified as abnormal patients by the framework.
  1. False negative (FN): It denotes the nu+mber of abnormal patients wrongly classified as normal patients by the framework.
  2. Specificity: It is defines as percentage of normal patients classified correctly by the framework. It is determined as

Sensitivity: It is defined as the percentage of abnormal patients classified correctly by the model. It is determined as

Accuracy: It denotes the percentage of correctly classified patients. In the present work (4-class problem) it is determined as:-

Where h is the number of correctly classified rules as healthy, i the number of correctly classified rules as mild, j the number of correctly classified rules as moderate, j the number of correctly classified rules severe and N is the total number of rules in the system knowledge base.

The knowledge base of the Fuzzy System has 174 rules with four classes as shown in (Table 18). These rules were validated against the human expert driven data collected Federal Teaching Hospital, Gombe State. (Table 5) has shown the general classes of patients. There were forty four rules classified as healthy and forty eight rules classified as mild which were considered for normal or healthy patients. So total number of rules classified for healthy patient is ninety two as shown in (Table 6). while for abnormal patients, forty two rules were classified as moderate and forty rules as severe. So total number of rules classified for abnormal patients are forty-one as shown in (Table 7).

Table 5: General Classes of patients.

Table 6: Classes of normal patients.

Table 7: Classes of Abnormal Patients.

Out of forty four rules classified as healthy, thirty eight rules were correctly in conformity with the expert system, so only four rules were not in conformity with the data. While Out of forty eight rules classified as mild, forty six rules were correctly in conformity with the expert system, so only two rules were not in conformity with the data. Therefore, the system correctly classified ninety two normal rules and however only six rules were wrongly classified as normal. While Out of the forty two rules classified as moderate, thirty six rules were correctly in conformity with data of the expert system, so only six rules were not in conformity with the data. While Out of forty rules classified as severe, thirty eight rules were correctly in conformity with data of the expert system, so only two rules were not in conformity with the data. Therefore, the system correctly classified seventy four abnormal rules and however eight rules were wrongly classified as abnormal. The validation result of the expert system is as follows:-

  1. True positive (TP) = 37
  2. True negative (TN) = 84
  3. False positive (FP) = 4
  4. False negative (FN) = 6
  5. = 91.30%
  6. = 90.24%
  7. = 90.08%

Therefore, the system has achieved 90.08% overall accuracy which is very excellent, thus the accuracy determines the proportion of the total number of predictions that were correct. At the same time, the system has 91.30%accuracy to classify of normal patients correctly by adopting the proposed framework (specificity) and 90.24% accuracy to classify abnormal patients correctly by adopting the proposed framework (sensitivity). This showed that, the system performed efficient and excellently to diagnose CAD.

Conclusion

Coronary Artery Disease (CAD) is one of the deadliest diseases in the world and in Nigeria, the CAD is at the moment gaining much popularity following the rising number of health issues related to the disease, including higher death rate, which is mostly due to lack of proper awareness among the common people. Expert systems have been specifically applied in a variety of life sciences support systems development, ranging from storage and retrieval of medical records, diagnostics, up to expert knowledge/decision support systems. In this work, a fuzzy based expert system for supporting and completing human expertise in the diagnosis of CAD has been developed and evaluated. The system archived 90.08% accuracy, 91.30% specificity and 90.24% sensitivity respectively, which showed that, the system performed efficiently and excellently to diagnose CAD and it can deployed and used in the hospitals in Nigeria.

Acknowledgement

This work was supported by the Tertiary Education Trust Fund, Nigeria (TETFUND), as an Institution Based Research Fund (IBR) for Federal University of Kashere, Gombe State, Nigeria.

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