A Comparative Analysis of Heart Disease Prediction Using Machine Learning Approaches

Abstract

Escalating unhealthy lifestyles has led to a surge in common health diseases, notably cardiovascular ailments, a leading cause of human mortality with over 17 million annual fatalities. This study focuses on conducting data analytics within the domain of heart disease, which has become a progressively popular predictive field. The expanding availability of data in this area further emphasizes the significance of in-depth analysis for comprehensive insights and informed decision-making. Diverse strategies and methods have been explored by other researchers. Employing algorithms encompassing KNN, decision tree, and random forest, the authors prognosticate patient illnesses. The support vector machine (SVM) demonstrated superior accuracy among all algorithms. This research enhances heart disease prediction through varied algorithms, underscoring SVM's efficacy and data-driven approaches in addressing escalating health concerns.