Heart Disease Prediction using Machine Learning

Abstract

Heart Disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic regression, and even more Enhanced techniques, for example, Deep Learning. Key datasets, including the Cleveland Heart Disease Dataset, have been instrumental in developing predictive models. However, challenges such as data quality, interpretability, and generalizability persist. Integrating wearable technologies, enhancing model explainability, and adopting privacy-preserving methods like federated learning are essential for advancing ML in cardiology. This paper provides a roadmap for researchers to address current gaps and foster the development of efficient, real-time healthcare solutions. This review emphasizes the importance of integrating ML with wearable technologies, enhancing explainability, and adopting federated learning to overcome these limitations. By addressing these challenges, ML-based systems could revolutionize heart disease management, paving the way for personalized, real-time, and accurate healthcare solutions. This study aims to provide researchers and clinicians with information about the current status, gaps, and future directions in the integration of machine learning in Heart Disease Prediction.