Cardiovascular Disease Prediction Using Deep Learning

Abstract

Cardiovascular disease (CVD), ahead of all other causes of death worldwide in this era. There is an immediate need for accurate, reliable, and practically applicable ways of early detection and treatment of diseases, and it connects a number of hazards for cardiovascular disease. One common method for analyzing massive amounts of historical information in healthcare is data extraction. To assist physicians with CVD prediction, they employ a number of data mining and deep learning (DL) techniques to navigate complex medical data. This review paper critically examines the application of deep learning techniques in predicting cardiovascular diseases, emphasizing data collection, preprocessing, model selection, performance metrics, and challenges. The data-driven nature of DL models allows for the analysis of diverse patient information, contributing to more accurate risk assessments. The review achieves by discussing the challenges and limitations, emphasizing the importance of collaborative efforts to connect DL's potential in enhancing cardiovascular disease prediction and improving patient outcomes.

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