Heart Failure prediction on diversified datasets to improve generalizability using 2-Level Stacking

Abstract

Heart disease is a leading cause of death worldwide, and early detection is crucial for improving patient outcomes. Machine learning classifiers have shown promise in predicting heart disease using patient-specific factors such as demographic information, medical history, and lifestyle habits. In this paper, we aimed to evaluate the performance of machine learning classifiers in predicting heart disease using a combination of 5 different datasets such as Cleveland, Hungarian, Switzerland, Long Beach VA, and StatLog (Heart) Datasets available on IEEE data port. The two significant challenges are addressed in this work: 1) predicting heart failure using machine-learning models without eliminating any clinical features, which increases the risk of overfitting and can result in poor performance metrics, and 2) we propose a model that will provide remarkable accuracy regardless of the type of data, to offer model generalizability. Machine-learning algorithms such as logistic regression, decision trees, random forests, support vector machine, extreme gradient boosting, extra-tree, and K-nearest neighbor are applied for heart disease prediction. In addition, ensemble approaches such as majority voting, boosting, bagging, and stacking are employed. The performance of the classifiers is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results showed that the ensemble approach of stacking outperformed individual models, with an accuracy of 93.67%.