Ensemble Learning With Explainable AI for Improved Heart Disease Prediction Based on Multiple Datasets

Authors

Language

English

Publication Date

4-22-2025

Journal

Scientific Reports

DOI

10.1038/s41598-025-97547-6

PMID

40263348

PMCID

PMC12015489

PubMedCentral® Posted Date

4-22-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting models was compared to that of the individual base models. To ensure the robustness of the performance evaluation, we conducted a statistical analysis using the Friedman aligned ranks test and Holm post-hoc pairwise comparisons. The results indicated that the developed ensemble models, particularly stacking, consistently outperformed the other models, achieving higher accuracy and improved predictive outcomes. This rigorous statistical validation emphasised the reliability of the proposed methods. Furthermore, we incorporated explainable AI (XAI) through SHAP analysis to interpret the model predictions, providing transparency and insight into how individual features influence heart disease prediction. These findings suggest that combining the predictions of multiple models through stacking or voting may enhance the performance of heart disease prediction and serve as a valuable tool in clinical decision-making.

Keywords

Humans, Heart Diseases, Machine Learning, Ensemble Learning, Computational biology and bioinformatics, Cardiovascular diseases, Computer science, Heart disease prediction, Ensemble learning, Stacking, Voting, Explainable AI, SHAP

Published Open-Access

yes

Recommended Citation

, "Ensemble Learning With Explainable AI for Improved Heart Disease Prediction Based on Multiple Datasets" (2025). Faculty, Staff and Student Publications. 679.
https://digitalcommons.library.tmc.edu/uthshis_docs/679