Using a privately available dataset from kaggle.com, this research compares the performance of six well-known machine-learning approaches for predicting heart failure. which include Logistic Regression, Gradient Boosted Trees (GBT), Naïve Bayes, Random Forest (RF), and Tree Ensemble. Heart failure is a major public health problem and it is necessary to improve the treatment of heart disease patients to increase the rate of survival.