PREDICTIVE MODELING FOR CARDIO VASCULAR HEALTH: A SURVEY ON DEEP LEARNING APPROACHES

Abstract

Cardiovascular disease is the leading cause of mortality worldwide, affecting millions of individuals and healthcare systems. It is imperative to promptly identify and accurately diagnose issues in order to guarantee the success of interventions and treatment strategies. Predictive modeling has consistently demonstrated its capacity to significantly improve the accuracy and efficiency of cardiac disease detection when combined with robust deep learning algorithms. Deep learning incorporates a diverse array of concepts and methodologies. Recurrent neural networks, hybrids, and convolutional neural networks are among the models that fall under this category. Their work frequently necessitates the use of electrocardiograms (ECGs), medical images, patient records, and other intricate cardiovascular data. The application of optimization techniques, such as evolutionary algorithms and particle swarm optimization, enhances model performance, convergence speed, and interpretability. This investigation concentrates on the most recent developments in deep learning algorithms for the prediction of cardiac health. It concentrates on essential techniques, performance metrics, practical applications, and frequently employed datasets. Some of the potential obstacles that may arise when utilizing deep learning for predictive modeling include data types, model interpretability, and privacy. The objective of this study is to assist academicians and practitioners by examining current patterns, potential solutions, and future opportunities for the identification and prevention of cardiovascular disease through the application of deep learning.