Machine Learning and Statistical Methods for Predicting Survival of Patients with Heart Failure

Heart failure is a branch of heart disease, which causes millions of people to die worldwide. This research is an investigation of survival prediction of patients with heart failure. The type of this prediction is a binary classification problem. The machine learning models used in this paper are logistic regression, decision trees, random forests, support vector machines, and artificial neural networks. The methods for evaluating the performance of prediction models are k-fold and stratified k-fold cross-validation. The results of 2 cross-validation indicate that logistic regression has the best performance. In addition, according to the feature ranking method in the literature, it can be observed that the prediction of heart failure mainly relies on serum creatinine, ejection fraction, and follow-up time. The conclusion is that the logistic regression, which only involves features: serum creatinine, ejection fraction, and follow-up time, is well suited for predicting the survival of patients with heart failure.