Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study
Analyzing lifestyle behavioral factors in ASCVD risk with an ML model improves the predictive performance compared to traditional models. Personalized prevention strategies tailored to an individual's lifestyle can effectively reduce ASCVD risk.
Authors
- Jong‐Hee Sohn (ORCID: https://orcid.org/0000-0003-2656-9222)
- Hyojung Ahn (ORCID: https://orcid.org/0000-0002-7450-5728)
- Chulho Kim (ORCID: https://orcid.org/0000-0001-8762-8340)
- Hye-Jin Kim (ORCID: https://orcid.org/0000-0003-4834-6230)
- Jaejun Lee (ORCID: https://orcid.org/0000-0003-4402-9350)
- Sang‐Hwa Lee (ORCID: https://orcid.org/0000-0002-0609-1551)
- Seung‐Ho Shin (ORCID: https://orcid.org/0000-0001-5624-1821)
- Heeji Choi
Institutions
- Hallym University (KR)
- Hallym Polytechnic University (KR)
- Health Insurance Review and Assessment Service (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-06-16
- DOI
- https://doi.org/10.2196/74415
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94