Heart Disease Prediction Using Machine Learning and Deep Learning Techniques: A Comprehensive Review

Abstract: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually despite significant advancements in healthcare technologies. Early prediction and timely diagnosis play a crucial role in reducing mortality rates and improving patient outcomes. Conventional diagnostic approaches rely heavily on clinical expertise, laboratory investigations, and medical imaging, which are often time-consuming, expensive, and susceptible to human interpretation errors. Recent developments in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized cardiovascular disease prediction by enabling automated analysis of large-scale clinical datasets with high accuracy and efficiency. Machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, K-Nearest Neighbors, Gradient Boosting, and Extreme Gradient Boosting have demonstrated remarkable capability in predicting heart disease using structured clinical data. Simultaneously, deep learning architectures including Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Autoencoders, and Transformer-based models have shown superior performance in analyzing electrocardiograms, echocardiograms, cardiac MRI, CT scans, wearable sensor data, and multimodal healthcare information. This review comprehensively examines recent advancements in heart disease prediction. The study discusses traditional statistical approaches, modern machine learning algorithms, deep learning techniques, hybrid intelligent models, explainable artificial intelligence, federated learning, Internet of Medical Things (IoMT), cloud-based healthcare systems, and wearable monitoring technologies. Furthermore, this review summarizes the strengths, limitations, datasets, evaluation metrics, and prediction performance of recent studies through a comparative analysis. Existing research gaps, practical challenges, future research directions, and emerging opportunities are also highlighted to guide researchers toward developing reliable, interpretable, and clinically deployable heart disease prediction systems. The findings indicate that integrating explainable AI, multimodal learning, privacy-preserving federated learning, and real-time wearable monitoring represents the future direction of intelligent cardiovascular healthcare.

Keywords: Heart Disease Prediction, Machine Learning, Deep Learning, Cardiovascular Disease, Artificial Intelligence, Explainable AI, Internet of Medical Things, Federated Learning, Healthcare Analytics.