Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering
Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, demanding accurate and timely prediction methods. Recent advancements in artificial intelligence have shown promise in enhancing clinical decision-making for CVD diagnosis. However, many existing models fail to di...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/13/5/201 |
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