Coronary Artery Disease Diagnosis Using Optimized Adaptive Ensemble Machine Learning Algorithm

Abstract

Cardiovascular diseases (CVD) involving the heart and blood vessels are reported as the leading causes of mortality worldwide. Coronary Artery Disease (CAD) is a major group of CVD in which presence of atherosclerotic plaques in coronary arteries leads to myocardial infarction or sudden cardiac death. In the past decades, several research efforts have been made to better understand the etiology of CAD, which will enable effective CAD diagnosis and treatment strategies. In this study, we have proposed a novel Self Optimized and Adaptive Ensemble Machine Learning Algorithm for the diagnosis of CAD. In our proposed method, the system automatically selects the most appropriate machine learning models. Our main goal is to design an Optimized Adaptive Ensemble Machine Learning Algorithm that works in different CAD datasets with high accuracy even with raw dataset. One of the important aspects of the proposed method is that the solution works on real-time data without using any pre-processing techniques on the datasets. Throughout this research attempt, we obtained 88.38% accuracy using two publicly available CAD diagnosis datasets.