Résumé
Narrowed arteries block the blood flow to the heart muscle and other parts of the body, which can cause chest pain. Coronary arteries disease (CAD) can weaken the heart muscle causing heart failure, in which the heart cannot pump blood. A person with underlying diseases is more prone to get highly affected by COVID-19 because of the decreased immunity. Cardiovascular disease and coronary heart disease have been associated with worsened outcomes of COVID-19 patients. Thus, detecting CAD at a proper stage is crucial to avoid any further serious issues. This paper is an empirical analysis to predict stable angina for CAD using Histogram gradient boosting (HGB) and Adaboost (ADB) classifier algorithm and compared the performance with traditional Naïve Bayes (NB) algorithm. © 2023 IEEE.