Performance of Machine Learning Algorithms for Heart Disease Prediction: Logistic Regressions Regularized by Elastic Net, SVM, Random Forests, and Neural Networks

Heart disease, a medical condition caused by plaque buildup in the walls of the arteries, is the leading cause of death in the U.S. and worldwide. About 697,000 people suffer from this condition in the U.S. alone. This research project aims to assess and compare the performance of several classification algorithms for predicting heart disease so that the method can be considered as a clinical indicator of cardiovascular health. These methods include multiple logistic regression regularized with or without elastic nets, support vector machine, random forest, and artificial neural networks. A low prevalence of the disease is reflected in the data imbalance, and an oversampling technique is also suggested to deal with the computational challenges posed by this data imbalance.