Heart Anomalies Prediction Utilizing a Variety of Machine Learning Algorithms

Abstract

We are aware that cardiovascular diseases are very lethal, patients do not get enough time for treatment and the treatment is also expensive for most people. The goal of this study is to predict the likelihood of an acute heart attack using a variety of machine learning approaches, including K closest neighbour, logistic regression, random forest classifier, support vector machine, and XGB classifier. The accuracy score obtained by all the machine learning algorithms has been demonstrated with the help of a table.