Comparison of Machine Learning Models to Predict Heart Attack: A Review

Abstract

According to the latest report published by the World Health Organization (WHO), heart disease is becoming increasingly prevalent globally due to various reasons associated with modern lifestyle. The fatality rate due to cardiac arrest is over 18 million per year worldwide. Unfortunately, due to the large population and insufficient healthcare infrastructure, it is often not feasible to identify heart disease in its early stages and initiate treatment. However, with the emergence of AI, DL, and Soft Computing, it has become possible to investigate these health issues in the initial stage. Therefore, the primary objective of this research is to develop a solution to predict heart disease accurately and in advance. Machine learning has become a critical component in healthcare, and many researchers have published papers investigating appropriate algorithms to predict heart disease. After reviewing various research papers, it was observed that different algorithms yield different accuracies on the same or different datasets. In the research work, various machine learning approaches and analysis will be examined for better accuracy on a validated dataset.