A Review of Commonly used Machine Learning Classifiers in Heart Disease Prediction

Last couple of years a lot of people are dying because of heart related disease and now this is one of the most concerning and life-threatening disease all over the world. It is also a concerning matter for health industry. About one person dies from heart disease every minute in the modern era. As heart disease prediction is a critical task, there is a need to automate the prediction process to avoid risks associated with it and inform the patient in advance. So, there is need a system or technique to diagnose this disease with maximum accuracy. Machine learning algorithm and technique can be helpful for health care industry because it has the ability to analyze large and complex data set. In this paper, we will exhibit how to utilize various kinds of machine learning models likes Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF), Logistic Regression and predicts the chances of heart disease and classifies patient's risk.