Advancements in Heart Disease Prediction: A Comprehensive Review of ML and DL Algorithms

Heart disease remains a leading cause of mortality globally, emphasizing the need for effective early prediction. This paper provides a comprehensive review of both machine learning (ML) and deep learning (DL) algorithms employed in the prediction of heart disease. Traditional ML techniques, including Decision Trees, Support Vector Machines, and Logistic Regression, have been extensively used due to their interpretability and efficacy on structured data. On the other hand, DL algorithms, particularly Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders, have shown promise in handling large-scale unstructured data like medical images and time-series records. We present a detailed comparison of these algorithms based on accuracy, computational complexity, data requirements, and interpretability. While ML techniques demand meticulous feature engineering, DL methods require vast amounts of data and computational resources but offer automated feature extraction. The review highlights existing challenges, such as data quality, overfitting, and model interpretability. We conclude by discussing potential future directions, including the integration of emerging techniques and technologies. This review aims to guide researchers and medical professionals in selecting the most appropriate algorithms for their specific heart disease prediction tasks.