Optimizing myopia prediction in children and adolescents using machine learning: a multi-factorial risk assessment model
Although ML models showed limited predictive accuracy, they helped identify modifiable risk factors associated with childhood and adolescent myopia. These findings may inform the design of targeted prevention strategies and early behavioral interventions rather than serve as clinical diagnostic tools.
Authors
- Wei Zhu (ORCID: https://orcid.org/0000-0003-1167-5882)
- Yue Xi (ORCID: https://orcid.org/0009-0001-7794-3918)
- Xiaobin Wei (ORCID: https://orcid.org/0000-0001-8599-6131)
- Shengting Dai (ORCID: https://orcid.org/0000-0002-6836-1107)
- Qian Zhao (ORCID: https://orcid.org/0000-0003-2244-6516)
- Wenjing Yan (ORCID: https://orcid.org/0000-0001-5746-2474)
Institutions
- Shanghai University (CN)
- East China University of Science and Technology (CN)
- Yinchuan First People's Hospital (CN)
- Shanxi Normal University (CN)
- Zhongkai University of Agriculture and Engineering (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-13
- DOI
- https://doi.org/10.3389/fmed.2025.1672432
- Primary Topic
- Ophthalmology and Visual Impairment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00