Paper Key : IRJ************038
Author: Sukriti Gupta,Abhinandan Tripathi
Date Published: 01 Jul 2024
Abstract
Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate and early diagnostic tools. Recent advancements in machine learning (ML) offer promising avenues for enhancing heart disease prediction, leveraging vast amounts of clinical data to improve diagnostic accuracy and patient outcomes. This comprehensive review examines the latest ML techniques and their applications in predicting heart disease. We explore a wide range of ML algorithms, including traditional methods like logistic regression, decision trees, and support vector machines, as well as advanced approaches such as neural networks, ensemble methods, and deep learning. The review also addresses the integration of various data types, from electronic health records and medical imaging to wearable device data, highlighting the potential of multimodal data fusion in predictive modeling. Furthermore, we discuss the challenges associated with ML applications in cardiology, including data quality, model interpretability, and ethical considerations. By synthesizing recent research findings, this review aims to provide a comprehensive understanding of how ML techniques are revolutionizing heart disease prediction, paving the way for more personalized and effective healthcare solutions.
DOI Requested