Predicting Cardiovascular Risk with Artificial Intelligence and Deep Learning

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 dat...

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Bibliographic Details
Published in2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) pp. 469 - 472
Main Authors Hou, Kung-Hsu, Chao, Chung-Hsing
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.02.2024
Subjects
artificial intelligence
ASCVD
Atherosclerosis
Computational modeling
Decision making
Deep learning
Electric potential
Neural networks
Predictive models
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DOI10.1109/ACCTCS61748.2024.00088

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Summary: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.
DOI:10.1109/ACCTCS61748.2024.00088