A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure

Abstract

The chapter explores the supervised machine learning techniques used in different investigations to predict whether a patient will have a heart failure or not. The research focuses on the articles that used the “Cleveland Heart Disease Data Set” from the UCI Machine Learning Repository and performs a comparison of the different techniques used to find the best performance. A conclusion of the best technique is also provided in this chapter. Some examples of the techniques are C4.5 tree, Naïve Bayes, Bayesian Neural Networks (BNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Nearest Neighbor (KNN).