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Informatics
Volume 12
Issue 4
10.3390/informatics12040110
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support

by
Brandon N. Nava-Martinez
,
Sahid S. Hernandez-Hernandez
,
Denzel A. Rodriguez-Ramirez
,
Jose L. Martinez-Rodriguez
,
Ana B. Rios-Alvarado
*,
Alan Diaz-Manriquez
,
Jose R. Martinez-Angulo
and
Tania Y. Guerrero-Melendez
Faculty of Engineering and Science, Autonomous University of Tamaulipas, Ciudad Victoria 87000, Mexico
*
Author to whom correspondence should be addressed.
Informatics 2025, 12(4), 110; https://doi.org/10.3390/informatics12040110 (registering DOI)
Submission received: 19 August 2025 / Revised: 7 October 2025 / Accepted: 8 October 2025 / Published: 11 October 2025
(This article belongs to the Special Issue Health Data Management in the Age of AI)
Versions Notes

Abstract

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care.
Keywords: ensemble models; heart disease; metamodel; heart attack prediction; hyperparameter tuning ensemble models; heart disease; metamodel; heart attack prediction; hyperparameter tuning

Share and Cite

MDPI and ACS Style

Nava-Martinez, B.N.; Hernandez-Hernandez, S.S.; Rodriguez-Ramirez, D.A.; Martinez-Rodriguez, J.L.; Rios-Alvarado, A.B.; Diaz-Manriquez, A.; Martinez-Angulo, J.R.; Guerrero-Melendez, T.Y. Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support. Informatics 2025, 12, 110. https://doi.org/10.3390/informatics12040110

AMA Style

Nava-Martinez BN, Hernandez-Hernandez SS, Rodriguez-Ramirez DA, Martinez-Rodriguez JL, Rios-Alvarado AB, Diaz-Manriquez A, Martinez-Angulo JR, Guerrero-Melendez TY. Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support. Informatics. 2025; 12(4):110. https://doi.org/10.3390/informatics12040110

Chicago/Turabian Style

Nava-Martinez, Brandon N., Sahid S. Hernandez-Hernandez, Denzel A. Rodriguez-Ramirez, Jose L. Martinez-Rodriguez, Ana B. Rios-Alvarado, Alan Diaz-Manriquez, Jose R. Martinez-Angulo, and Tania Y. Guerrero-Melendez. 2025. "Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support" Informatics 12, no. 4: 110. https://doi.org/10.3390/informatics12040110

APA Style

Nava-Martinez, B. N., Hernandez-Hernandez, S. S., Rodriguez-Ramirez, D. A., Martinez-Rodriguez, J. L., Rios-Alvarado, A. B., Diaz-Manriquez, A., Martinez-Angulo, J. R., & Guerrero-Melendez, T. Y. (2025). Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support. Informatics, 12(4), 110. https://doi.org/10.3390/informatics12040110

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