Development of a machine learning-based predictive model for osteoporosis risk and its application in clinical decision support
The osteoporosis prediction model developed in this study achieved quantitative risk estimation and interpretable outputs using a limited set of features, providing a feasible technical approach for early screening of osteoporosis. Future work should focus on external validation and recalibration in multicenter populations to further evaluate and optimize the model's predictive performance and clinical applicability.
Authors
- Ling Cheng
- Qinqin Deng (ORCID: https://orcid.org/0000-0002-5518-0796)
- Zichen Shao
- Weikang Sun (ORCID: https://orcid.org/0000-0001-5204-2403)
- Huanan Li (ORCID: https://orcid.org/0000-0002-8834-4320)
- Weidong Liang (ORCID: https://orcid.org/0000-0001-5929-5542)
- Xin Huang (ORCID: https://orcid.org/0000-0002-6569-0095)
- Jianfeng Wu (ORCID: https://orcid.org/0000-0001-6003-9810)
Institutions
- Jiangxi University of Traditional Chinese Medicine (CN)
- Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-13
- DOI
- https://doi.org/10.3389/fmed.2025.1680731
- Primary Topic
- Bone health and osteoporosis research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Commission of Jiangxi Province