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

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:30:01Z
Fecha de publicación2025-01-01
ResumenCardiovascular 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.es_ES
Doihttps://doi.org/10.3390/informatics12040110es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1950
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónInformaticses_ES
URL relacionadohttps://doi.org/10.3390/informatics12040110es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInformatics
TítuloHeart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Supportes_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorNava-Martinez, Brandon N.
AutorHernandez-Hernandez, Sahid S.
AutorRodriguez-Ramirez, Denzel A.
AutorMartinez-Rodriguez, Jose L.
AutorRios-Alvarado, Ana B.
AutorDiaz-Manriquez, Alan
AutorMartinez-Angulo, Jose R.
AutorGuerrero-Melendez, Tania Y.
AutorNava-Martinez, Brandon N.es_ES
AutorHernandez-Hernandez, Sahid S.es_ES
AutorRodriguez-Ramirez, Denzel A.es_ES
AutorMartinez-Rodriguez, Jose L.es_ES
AutorRios-Alvarado, Ana B.es_ES
AutorDiaz-Manriquez, Alanes_ES
AutorMartinez-Angulo, Jose R.es_ES
AutorGuerrero-Melendez, Tania Y.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número4es_ES
Rango de páginas110es_ES
URL relacionadahttps://doi.org/10.3390/informatics12040110
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen12es_ES

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