Performance Analysis by Using the Knime Analytical Platform to Forecast Heart Failure Using Several Machine Learning Methods

Abstract

Abstract: 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), Naive 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. Delicacy was used to assess the performance of machine learning methods. RF produced the highest performance score of 80% when compared to Decision Tree Classifier and Tree Ensemble, Gradient BoostedTrees (GBT), Naive Bayes, and Logistic Regressions.