Comparative Analysis of Machine Learning Models for Prediction of Heart Attacks

Heart attack prediction is a crucial task in the area of cardiovascular disease prevention. In last few years, there is a significant increase in research on developing machine learning models for heart attack prediction using various clinical and non-clinical risk factors. The aim of these models is to identify who among the peoples are at high risk of heart attack in future. The healthcare providers is providing prevention measures and interventions. In this paper, we outline three techniques for performing comparative analyses that produced worthwhile outcomes. The outcome of this analysis shows that machine learning algorithms work more effectively. All over the prediction of heart attack research is holding promise to improve health checkups of patient and to reduce the cardiovascular disease risks on the patients’ healthcare system.