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Deep learning for cardiovascular disease prediction: Recent advances, challenges and future directions
- 1 School of Computer Science and Engineering, University of New South Wales
* Author to whom correspondence should be addressed.
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.
Keywords
Deep Learning, Disease Prediction, Cardiovascular Disease, Public Health.
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Cite this article
Wu,Y. (2024). Deep learning for cardiovascular disease prediction: Recent advances, challenges and future directions. Theoretical and Natural Science,62,24-32.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Biological Engineering and Medical Science
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