Development of an interpretable machine learning model for early prediction of aortic stiffness risk in health examination populations
This study developed a high-performance XGBoost model based on routine health examination indicators and further implemented an accessible web-based calculator to support clinical decision-making in settings without direct access to cfPWV measurement. The calculator is available at: https://lgezijo5wyivqrnt2zrkcm.streamlit.app/.
Authors
- Xun Sun (ORCID: https://orcid.org/0000-0002-4316-2988)
- Ting Li (ORCID: https://orcid.org/0000-0002-2027-9462)
- Wen Zhang (ORCID: https://orcid.org/0000-0001-5166-0736)
- Zuchang Ma
- Yuxia Li
- Zhensen Zhou
- Hongmei Qian
- Fuying Kong
Institutions
- Anhui Medical University (CN)
- Chinese Academy of Sciences (CN)
- Hefei Institutes of Physical Science (CN)
- Institute of Intelligent Machines (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Cancer Hospital of Chinese Academy of Medical Sciences (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2026-01-07
- DOI
- https://doi.org/10.3389/fcvm.2025.1730409
- Primary Topic
- Cardiovascular Health and Disease Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China