Deep Learning for Cardiovascular Disease Prediction: Recent Advances, Challenges and Future Directions
Yonghui Wu
Abstract
Abstract. This paper reviews the application of deep learning methods in cardiovascular disease (CVD) prediction, comparing their performance with traditional statistical and machine learning models. We focus on the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in processing medical images and ECG signals, respectively. The reviewed studies demonstrate the superior performance of deep learning in capturing complex patterns and making accurate predictions. However, challenges related to data quantity, diversity, generalizability, and model interpretability still remain. Future research should focus on enhancing data representation, model comparison, and explainable AI to improve the efficiency and applicability of deep learning in clinical practice.