key: cord-0057682-a3uomje9
authors: Hossain, Sohrab; Sarma, Dhiman; Chakma, Rana Joyti; Alam, Wahidul; Hoque, Mohammed Moshiul; Sarker, Iqbal H.
title: A Rule-Based Expert System to Assess Coronary Artery Disease Under Uncertainty
date: 2020-06-08
journal: Computing Science, Communication and Security
DOI: 10.1007/978-981-15-6648-6_12
sha: 16acdbc055b688819324e9b55bb9c525791ce382
doc_id: 57682
cord_uid: a3uomje9

The coronary artery disease (CAD) occurs from the narrowing and damaging of major blood vessels or arteries. It has become the most life-threatening disease in the world, especially in the South Asian region. Its detection and treatment involve expensive medical facilities. The early detection of CAD, which is a major challenge, can minimize the patients’ suffering and expenses. The major challenge for CAD detection is incorporating numerous factors for detailed analysis. The goal of this study is to propose a new Clinical Decision Support System (CDSS) which may assist doctors in analyzing numerous factors more accurately than the existing CDSSs. In this paper, a Rule-Based Expert System (RBES) is proposed which involves five different Belief Rules, and can predict five different stages of CAD. The final output is produced by combining all BRBs and by using the Evidential Reasoning (ER). Performance evaluation is measured by calculating the success rate, error rate, failure rate and false omission rate. The proposed RBES has higher a success rate and false omission rate than other existing CDSSs.

(chest pain), and lead to a heart attack by injuring heart muscle. The death toll due to process uses belief-rule-base for modeling clinical domain knowledge, and applies an evidential reasoning approach for implementing reasoning. Studies show that RIMER based clinical decision support systems are highly efficient in supporting and interacting with clinical domain knowledge under uncertainty. In [28] , Multi-Criteria Decision Making Methods were presented for accessing CAD under uncertainty where presence and absence of CAD is predicted through using symptom and signs of CAD. But these approaches report neither the number of blocked arteries nor the significance of severity of the disease [8, 16, 26, 28, 29] . Weak parameters, like signs and symptoms, are used for predicting CAD as well as for predicting the similar types of diseases like mitral regurgitation, dilated cardiomyopathy, congenital heart disease, hyper-tropic cardiomyopathy, myocardial infarction etc. Some researchers developed the Medical Decision Support System (MDSS) to predict CAD. Other proposer polygenic risk scores (PRS), a nonlinear, for CAD prediction with accuracy an 0.92 under the receiver operating curve (AUC) [8] .

Experimental analysis reveals that CAD diagnosis and its severity can be predicted significantly through clinical features along with pathological and demographic features [23, 25, 26, 28] . In this paper, we consider all these parameters, and proposed a cooperative-belief-rule based prototype (CDSS) to assist doctors for CAD analysis under uncertainty.

In this paper, five separate BRBs are developed based on five distinct feature sets of patients such as i) patients' pathological features, ii) patients' physiological features, iii) patients' demographic features, iv) patients' behavioral features, and v) patients' non-modifiable risk factors. The BRBs are as follows:

(1) 

Where u is utility value, a ij is individual matching degree, A ij is j th referential value for i th attribute, and x i is the input for i th antecedent (Fig. 1) . To calculate activated weight to each rule the following equation is used:

Where w k is the k th rule's activation weight and a k is the interrelation between attributes. To calculate a k, the following equations is used:

Whereδ i is the antecedent weight and α k i represents individual matching degrees for i th attribute. Five separate BRBs to predict CAD are BRB_P, BRB_PH, BRB_D, BRB_B, and BRB_N. BRB_PH considers physiological factors like blood pressure and stress. BRB_P considers pathological factors like blood sugar level, low density lipoprotein, and triglyceride level. BRB_D considers factors like age and body mass index. BRB_B considers behavior factors like diet, smoking, and physical activities. BRB_N considers non-modifiable risk factors like gender, family history, and residential Area.

Attributes like blood pressure, stress, blood sugar, lipoprotein, triglyceride, age, body mass index, unhealthy diet, smoking, family history, and race are categorized into five classes, namely Physiological, Pathological, Demographical, Behavioral, and Nonmodifiable risk factors. All the attributes have uncertainties at some level except gender attribute (Table 1) . 

Five different types of attributes have been considered in this research. Explanations of the numerical values of each attribute are as follows:

Blood Pressure (BP) The blood pressure which creates heartbeats is known as BP. For BP, several numerical points namely Usual, Elevated, Hypertension Stage 1, Hypertension Stage 2, Hypertension Stage 3 are reflected and shown in Table 2 . Here, the referential numerical points are presented as in the Eq. (10).

Stress Score (SS) Intermediate risk of heart problems can be expressed in the SS score. It can be distributed into several referential numerical points, namely regular, mildly irregular, moderately irregular and severely irregular are shown in Table 3 . 

Blood Sugar Level. It is the amount of sugar in the blood. Five referential points are shown in Table 4 and expressed by the Eq. (12). 

Its measures triglycerides amount in blood. Four referential points are described in Table 5 and Eq. (13). 

Low Density Lipoprotein (LDL) It contains both lipid and protein, and carries cholesterol to body tissues. Five referential values related to LDL are shown in Table 6 and Eq. (14) . 

Age Older people are more likely to be victims of coronary artery disease, especially, after the age of 65 years. Usually, aged people have higher chance of getting CAD (Table 7 ). 

It indicates the amount of fat ratio. It is applicable for the age range from 18 to 65. It is the ratio of weight to height (Table 8) .

Four referential values, namely, healthy weight (18.5-24.9), overweight (25-29.9), obese (30-39.9), and morbidly obese (>=40), have been considered in the following equation from the above table. D2 {H, O, OB, MO} Obese (OB) 30-39.9

Morbid Obese (MO) > = 40

Mediterranean diet can reduce the risk of CAD by 30%. It is mainly plant based food and categorized into four sections shown in Table 10 and expressed by Eq. (17) ( Table 9 ). 

Inactive and less active people are at high risk to develop CAD. Physical activities are categorized into four sections which are shown in Table 11 and expressed by Eq. (19) . 

Male has higher risk of CAD than female. Besides, male suffers from CAD in earlier age than female. But after the age of 70 years, both males and females have similar chances of getting heart disease (Table 12) . 

If parents have histories of heart disease, children have a high risk of developing CAD. The risk is even higher if parents have suffered before early 50 years of age. The numerical points for the family history are represented by 0 (No history of parent's heart disease), 1 (History of parent's heart disease), and 2 (History of parent's heart disease before age of 50), and expressed in Table 13 and by Eq. (21) . 

People from mega-cities are more prone to CAD. This is because of a higher rate of diabetes and obesity. On the other hand, people from hill track areas are less likely to develop heart disease. The numerical points for the residential areas are 0(Mega City), (Rural Area), and 2 (Hill track area), and expressed in Table 14 and by Eq. (22). 

All attributes from Eqs. (10) to (22) are applied as input variables to predict the CAD class. Sub rules 1 to 20 are expressed in Table 15 for the two Physiological factors from Eqs. (10) and (11) . A sub rule of the CAD can be shown as: R3: IF blood pressure is usual AND stress score is significantly irregular THEN Overall Prediction is {(Stage 1, 0.6), (Stage 2, 0.3), (Stage 3, 0.1), (Stage 4, 0.0), (Stage 5, 0.0)} In the R3, the antecedent attributes are and the consequence attributes are. The rule shows that patient with usual blood pressure and significantly irregular stress score has the probability of developing CAD are (Stage 1 is 60%), (Stage 2 is 30%), (Stage 3 is 10%), (Stage 4 is 0%), (Stage 5 is 0%). The summation of all referential values for R3 is (0.6 + 0.3 + 0.1 + 0.0 + 0.0 =) 1. If the summation of all referential values for a particular rule is 1, we can say that the rule is competed. For some missing attributes or ignorance, the summation may be less than 1 and the rule is incomplete [34] . 

Dataset was collected from the National Heart Foundation, Bangladesh with proper authorization. The data set description is shown in Table 16 . 

In the binary diagnostic test, a positive or negative diagnosis is made for each patient. When the result of diagnosis is compared to the true condition, we find four possible outcomes: true positive, true negative, false positive, false negative.

It is the ratio of correctly identified patient' numbers and total patients. Equation (23) 

It is the ratio of incorrectly identified patients' numbers and total patients. Equation (24) is used to calculate the error rate and average error rate.

Error Rate = Number of patients incorrectly identified Total number of patients * 100% (24)

It is the ratio of the number of non-recognized patients to total patients. Equation (25 

Heart disease is one of the major threats to public health and the reason for the main cause of death worldwide. Although numerous researches are carried out in this area, still there are challenges to diagnose CAD for treatment. In this paper, the proposed expert system results in an average accuracy rate of 89.90% which is the highest among other existing CDSS. The average false omission rate (3.54%) is also the lowest in this system than that of other CDSS. Our test results satisfy one of the main goals of this research. The average failure rate (11.10%) and average error rate (8.81%) also remain as marginal. Class E (silent Ischemia) success rate is the lowest among all classes. The reason is that Class E occurs suddenly without showing any warning signs of heart problems. It was noted that Class E is common to people with diabetes. It requires further research work to investigate whether or not diabetes influences the success rate in Class E type patients. Apart from this, our research concludes that RBES has a higher success rate and false omission rate than other existing CDSS.

Heart disease and stroke statistics-2017 update: a report from the American Heart Association

Cardiovascular disease in Bangladesh: a review

Respiratory and metabolic acidosis correction with the advanced organ Support system

IEEE: Fully-convolutional deeplearning based system for coronary calcium score prediction from non-contrast chest ct

Detection of coronary calcium during standard chest computed tomography correlates with multidetector computed tomography coronary artery calcium score

In-field detection of Altemaria solani in potato crops using hyperspectral imaging

Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning

Online updating belief-rule-base using Bayesian estimation. Knowledge-Based Syst

Development of genome-derived tumor type prediction to inform clinical cancer care

IEEE: Associating risks of getting strokes with data from health checkup records using dempster-shafer theory

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study

Can we improve prediction of adverse surgical outcomes? development of a surgical complexity score using a novel machine learning technique

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

A medical decision support system for The Prediction Of The Coronary Artery Disease Using Fuzzy Cognitive Maps

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

A cooperative belief rule based decision support system for lymph node metastasis diagnosis in gastric cancer. Knowledge-Based Syst

A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer. Knowledge-Based Syst

Feature selection and instance selection from clinical datasets using co-operative co-evolution and classification using random forest

A highly accurate firefly based algorithm for heart disease prediction

An optimal safety assessment model for complex systems considering correlation and redundancy

A belief rule based expert system to assess tuberculosis under uncertainty

Nonparametric competing risks analysis using bayesian additive regression trees

Probabilistic graphical modeling for estimating risk of coronary artery disease: applications of a flexible machine-learning method

Reliability assessment model for industrial control system based on belief rule base

Multi criteria decision making methods to predict the prevalence of coronary artery disease

Calcification detection using deep structured learning in intravascular ultrasound image for coronary artery disease

Context-aware rule learning from smartphone data: survey, challenges and future directions

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

BehavDT: a behavioral decision tree learning to build user-centric context-aware predictive model

Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

A belief rule based expert system to predict student performance under uncertainty