Sultanul Arifeen Hamim; Rakin S. Aftab; M. Ahmed; Farzana Faiza; M. F. Mridha

Advanced Heart Attack Prediction Using a Stacked Ensemble Machine Learning Model and Diverse Data Integration

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Author(s)

Sultanul Arifeen Hamim ¹ Rakin S. Aftab ^1,* M. Ahmed ¹ Farzana Faiza ¹ M. F. Mridha ¹

1. Department of Computer Science, American International University-Bangladesh, Dhaka, 1219, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2025.05.04

Received: 3 Jun. 2025 / Revised: 15 Jul. 2025 / Accepted: 21 Aug. 2025 / Published: 8 Oct. 2025

Index Terms

Myocardial Healthcare, Heart Attack Prediction, Machine Learning, Data Integration, Hybrid Stacked Ensemble Model

Abstract

Heart attacks continue to be one of the primary causes of death globally, highlighting the critical need for advanced predictive models to improve early diagnosis and timely intervention. This study presents a comprehensive machine learning (ML) approach to heart attack prediction, integrating multiple datasets from diverse sources to construct a robust and accurate predictive model. The research employs a stacking ensemble model, which combines the strengths of individual ML algorithms to improve overall performance. Extensive data preprocessing steps were carefully undertaken to preserve the dataset's integrity and maintain its quality. The results demonstrate a superior accuracy of 97.48%, significantly outperforming state-of-the-art approaches. The high level of accuracy indicates the model’s potential effectiveness in the clinical setting for early detection of heart attack and prevention. However, the proposed model is influenced by the quality and diversity of the integrated datasets, which could affect its generalizability across broader populations. Challenges encountered during the model's development include optimizing hyperparameters for multiple classifiers, ensuring data preprocessing consistency, and balancing computational efficiency with model interpretability. The results underscore the pivotal contribution of advanced ML approaches in revolutionizing the management of cardiovascular attack. By addressing the complexities and variabilities inherent in heart attack prediction, the work provides a pathway towards more effective and personalized cardiovascular disease management strategies, demonstrating the transformative potential of ML in healthcare.

Cite This Paper

Sultanul Arifeen Hamim, Rakin S. Aftab, M. Ahmed, Farzana Faiza, M. F. Mridha, "Advanced Heart Attack Prediction Using a Stacked Ensemble Machine Learning Model and Diverse Data Integration", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.5, pp.49-67, 2025. DOI:10.5815/ijisa.2025.05.04

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