A Review on Recent Machine Learning Algorithms Used in CAD diagnosis

Abstract

Coronary Artery Disease (CAD) is a cardiovascular disease that has the highest mortality rate. The non-invasive method for diagnosing CAD is coronary angiography which is expensive and exposes the patient to radiation. The present scenario for diagnosing CAD has motivated researchers to analyze and diagnose the disease using Machine Learning (ML) algorithms. In this work, the efficacy of various ML algorithms used to diagnose CAD disease is presented and compared. The proposed methodology aims to train the dataset using algorithms namely: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Tree, Extreme Gradient Boosting, Random Forest, Extra Tree Classifier, and Light Gradient Boosting machine, to achieve accuracy for diagnosing the CAD. The experimental results are validated in the simulation environment, and the conclusions were drawn from the performance indices, i.e., Accuracy, Sensitivity, Specificity, Precision, F1-Score, and Binary cross-entropy cost function. The meta-analysis shows that Light Gradient Machine achieves an accuracy of 93.36%, which is the highest among other ML algorithms.