Measuring the Heart Attack Possibility using Different Types of Machine Learning Algorithms

Abstract

The heart seems to be a very complicated organ in human body. If some part of the heart has been seriously damaged, the remaining part of the heart will still remain functioning. But as a result of the injury, the heart can be weakened and unable to pump as much blood as normal. With timely detection of multiple possible hamstring issues, proper care, and dietary changes after a heart attack, the additional injury can be reduced or avoided. In this paper, different types of machine learning algorithms are used for measuring the possibility heart attack, they are logistic regression, random forest, bagging, MLP, and decision tree. By finding the best algorithm, this paper also shows the correlation matrices, visualizes the feature, and AUC. From this research work, it is evident that the logistic regression is the best model with an accuracy of about 80% and also gives the best AUC of about 87%.