Machine Learning in Heart Disease Prediction: A Comprehensive Investigation and Future Prospects

Abstract

This paper provides a systematic review of machine learning (ML) and deep learning (DL) applications in heart disease prediction, highlighting advancements, challenges, and future prospects. Traditional ML models, including Random Forests, Support Vector Machines, and ensemble methods, have demonstrated strong performance in feature extraction and risk classification. Deep learning approaches, such as Graph Neural Networks (GNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures, further enhance predictive accuracy by capturing complex relationships in multi-dimensional medical data. Despite their success, critical limitations persist, including the "black-box" nature of neural networks, which hampers clinical interpretability; data heterogeneity across regions and institutions, limiting model generalizability; and privacy risks associated with centralized medical data training. To address these challenges, emerging solutions like interpretable rule-based systems, domain adaptation frameworks, and federated learning with differential privacy are proposed. The review underscores the need for interdisciplinary collaboration to integrate clinical expertise with advanced AI techniques, ensuring robust, transparent, and ethically compliant tools for early heart disease diagnosis. Future research should prioritize model interpretability, cross-institutional adaptability, and secure data-sharing mechanisms to bridge the gap between theoretical innovation and clinical implementation.

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