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M. F. Mridha

Work place: Department of Computer Science, American International University-Bangladesh, Dhaka, 1219, Bangladesh

E-mail: firoz.mridha@aiub.edu

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Biography

M. F. Mridha (Senior Member, IEEE) is currently working as an Associate Professor in the Department of Computer Science, at American International University-Bangladesh (AIUB). Before that, he worked as an Associate Professor and Chairman in the Department of Computer Science and Engineering, at Bangladesh University of Business and Technology (BUBT). He also worked as a CSE department faculty member at the University of Asia Pacific and as a graduate head from 2012 to 2019. He received his Ph.D. in AI/ML from Jahangirnagar University in 2017. His research experience in academia and industry has resulted in over 120 journal and conference publications. His research work contributed to the reputed Journal of Scientific Reports Nature, Knowledge-Based Systems, Artificial Intelligence Review, IEEE Access, Sensors, Cancers, Biology and Applied Sciences, etc. His research interests include artificial intelligence (AI), machine learning, deep learning, and natural language processing (NLP). For more than 10 (Ten) years, he has been with the master’s and undergraduate students as a supervisor of their thesis work. His research interests include artificial intelligence (AI), machine learning, natural language processing (NLP), big data analysis, etc. He has served as a program committee member in several international conferences/workshops. He served as an Academic editor of several journals including PLOS ONE Journal. He has served as a reviewer of reputed journals like IEEE Transactions on Neural Networks, IEEE Access, Knowledge Base System, Expert System, Bioinformatics, Springer Nature, MDPI, etc., and conferences like ICCIT, HONET, ICIEV, IJCCI, ICAEE, ICCAIE, ICSIPA, SCORED, ISIEA, APACE, ICOS, ISCAIE, BEIAC, ISWTA, IC3e, ISWTA, CoAST, icIVPR, ICSCT, 3ICT, DATA21, etc.

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

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

DOI: https://doi.org/10.5815/ijisa.2025.05.04, Pub. Date: 8 Oct. 2025

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.

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