Machine Learning Techniques for Heart Failure Prediction

This paper discusses the performance of four popular machine learning techniques for predicting heart failure using a publicly available dataset from kaggle.com, which are Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). They were selected due to their good in medical-related applications. Heart is common public health problem, there need improve management cases increase survival rate. The vast amount medical data related availability powerful computing devices allow researchers conduct more experiments. was measured by accuracy, precision, recall, f1-score, sensitivity, specificity with 13 symptoms or features. Experimental analysis showed that RF produces highest score, 0.88 compared SVM, NB, LR. Further experiments also conducted determine important features failure, results indicated all important.