Coronary Artery Disease Diagnosis Using Supervised Fuzzy C-Means with Differential Search Algorithm-based Generalized Minkowski Metrics

Abstract

Introduction: Coronary Artery Disease (CAD), one of the leading causes of death, is narrowing the walls of the coronary arteries. Angiography is the most accurate but invasive and costly CAD diagnosis method associated with mortality. The aim of this study was to design a computer-based non-invasive CAD diagnosis system. Methods: In this work, a dataset from Cleveland clinic foundation, containing 303 patients and 20 features, was used. Supervised Fuzzy C-means (SFCM) classification was used to design a classifier for CAD diagnosis. The Generalized Minkowski Metrics (GMM) was used to handle objects containing different measurement scale features. The performance of the SFCM was assessed with/without Statistical Feature Selection (SFS). The weights of the GMM, i.e. the significance of different features, beside other classifier parameters were tuned using Differential Search Algorithm (DSA), and the validity of the proposed classifier was further investigated. The hold-out and 10-fold cross validation were used for the performance assessment. Result: The average accuracy of the base classifier (SFCM + GMM) was 79% (hold-out validation). It increased to 82% when using SFS. The average accuracy, sensitivity and specificity of the DSA-based classifier were 88%, 86% and 88%, respectively (cross-validation). Conclusion: The most important features were the number of major vessels colored by fluoroscopy, the family history of CAD, peak exercise systolic blood pressure, maximum exercise heart rate achieved, chest pain type, resting heart rate, Fasting Blood Sugar and gender. This classifier showed substantial agreement with the angiographic results. The hybrid diagnosis system is thus promising. However, it is necessary to improve its reliability. The CAD risk factors have been identified over the past several decades include abnormal levels of circulating cholesterol, hypertension, cigarette smoking, diabetes, male gender, postmenopausal state, advancing age, sedentary lifestyle, obesity, and a positive family history of premature vascular disease. Moreover, new risk factors have been emerged as elevated blood levels of homocysteine, fibrinogen, inflammation and infection, atherogenic lipoprotein phenotype, elevated levels of lipoprotein, insulin resistance syndrome, psychosocial factors and a number of genetic polymorphisms [5]. There are several diagnostic tools for CAD [6-8]. Some of the general diagnostic tests include physical examination, lab tests, Electrocardiogram (ECG), echocardiogram, stress test, electron beam computed tomography, coronary angiography and cardiac catheterization. One of the major limitations of ECG is the undiagnosed symptoms of CAD. On the other hand, another alternative invasive methodology, angiogram, is painful and discomfort to the patients. Furthermore, the above mentioned procedures take a lot of cost, time and effort [9].

Utilizing Fuzzy C-Means Clustering and PCA in Public Health: A Machine Learning Approach to Combat CVD and Obesity

“…[16] discussed a machine learning approach for obesity risk prediction, acknowledging its potential in preventing obesity-related diseases. [17] presents a novel computer-based system for diagnosing Coronary Artery Disease using a hybrid approach that combines Supervised Fuzzy C-Means clustering with a Differential Search Algorithm-based Generalized Minkowski Metrics, showing high agreement with angiographic results.…”

Metaheuristic Algorithms for Optimization: A Brief Review

“…A study by Negahbani et al [4] utilized the differential search algorithm in conjunction with fuzzy c-means to diagnose coronary artery disease and achieved promising results in terms of accuracy and sensitivity. The binary-operating backtracking algorithm designed by Zhang et al [5] leveraged the power of extreme learning machines for wind speed forecasting.…”

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

“…Gan and Duan [123] proposed a chaotic differential search algorithm for image processing and it has been combined with lateral inhibition to edge extraction and image enhancement. Negahbani et al [124] used differential search algorithm for the diagnosis of coronary artery disease with fuzzy c-means that was used as a classifier. The performance of the proposed approach has been evaluated using accuracy, sensitivity and specificity measures.…”