Predictive Modelling for Cardiovascular Health: A Survey on Deep Learning Approaches

Cardiovascular disease are the most frequently used cause of death in the world, with a big impact on health care systems and millions of human lives. Early diagnosis and accurate detection are important for effective intervention and treatment planning. Predictive modeling techniques in general have long been quite promising as applied to improving the accuracy and efficiency of heart disease detection, especially with new approaches in deep learning. Deep learning approaches involve all the technologies and concepts, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), hybrid approaches, and can be widely used to analyze complex cardiovascular data, such as electrocardiograms (ECGs), medical imaging, or clinical records. Optimization techniques, such as genetic algorithms and particle swarm optimization, are also used to improve performance, speed up convergence, and make the model more interpretable. This survey offers a complete review of the latest advances in deep learning methodologies for cardiovascular health prediction, with a focus on key methodologies, commonly used datasets, performance metrics, and real-world applications. In addition, we discuss challenges associated with predictive modeling using deep learning, which include data imbalance, model interpretability, and privacy. The paper aims at providing a valuable resource for the researcher and the practitioner, giving insights into current trends and potential solutions up to future directions in using deep learning for cardiovascular disease prediction and prevention.