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Author(s): Kazeem Sodiq, Otapo Akeem, Lateef Saliu, Peter Balogun, Latifat Oderinu, Solomon Salau, Oyesola Awoyoola.

Volume/Issue: Volume 9, Issue 2 (2026)

Page No: 1-19

Journal: British Journal of Computer, Networking and Information Technology (BJCNIT)

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Abstract:

Heart attack, clinically referred to as acute myocardial infarction (AMI), remains one of the leading causes of global mortality, largely due to delayed diagnosis and the complex interplay of cardiovascular risk factors. Early and accurate prediction of heart attacks is critical for reducing morbidity, guiding preventive interventions, and improving patient outcomes. Traditional diagnostic and predictive approaches—such as risk-score models, biomarker analysis, and imaging techniques—provide valuable insights but often lack the precision, adaptability, and predictive strength required for real-time clinical decision-making. Recent advancements in artificial intelligence (AI), particularly deep learning, have demonstrated remarkable potential in identifying subtle, nonlinear patterns within large medical datasets that may be overlooked by conventional methods.This paper presents a comprehensive and systematic review of traditional, machine learning, and deep learning techniques used for heart attack prediction. Key algorithms—including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Denoising Autoencoders (DAE)—are examined with respect to model design, dataset characteristics, evaluation metrics, interpretability, and clinical readiness. Findings from previous studies indicate that hybrid deep learning models consistently outperform standalone methods, achieving higher accuracy, sensitivity, and robustness. Despite these advancements, significant gaps remain in explainability, dataset diversity, real-world validation, and integration into clinical workflows.The review highlights the potential of AI-driven predictive systems to support early detection, reduce healthcare costs, and promote personalized cardiovascular care. It concludes by outlining future research directions focused on interpretable hybrid models, multimodal datasets, and deployment-ready systems capable of enabling proactive, patient-centered heart attack prevention.

Keywords:

Acute Myocardial Infarction, Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Heart Attack Prediction.

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