Predicting Cardiovascular Risk with Artificial Intelligence and Deep Learning - Hou, Kung-Hsu; Chao, Chung-Hsing (2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), 2024)
Through the application of artificial intelligence and deep learning techniques, this paper presents an innovative approach to predict the risk of developing atherosclerotic cardiovascular disease (ASCVD). We trained neural networks on a large dataset of electronic health records and risk factor data and evaluated their performance in predicting ASCVD risk in the next 10 years. Our results demonstrate that the neural networks achieved high accuracy, outperforming traditional risk prediction models. This approach has the potential to improve clinical decision-making in cardiovascular medicine and help clinicians develop more personalized treatment plans. Further research is needed to optimize the performance and generalizability of the neural network models for ASCVD risk prediction.