Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM.
Authors
- Zengyun Hu (ORCID: https://orcid.org/0000-0002-4259-9141)
- Shi Zhao (ORCID: https://orcid.org/0000-0001-8722-6149)
- Yu Zhao (ORCID: https://orcid.org/0000-0001-9025-2014)
- Xiaolong Li (ORCID: https://orcid.org/0000-0001-7493-2650)
- Fan Ding (ORCID: https://orcid.org/0000-0001-6511-5616)
- Li Feng (ORCID: https://orcid.org/0000-0003-3284-9247)
- Yiyi Zhao (ORCID: https://orcid.org/0000-0002-8766-1383)
- Lu Zhang (ORCID: https://orcid.org/0000-0003-4669-8240)
- Yuhong Zhang (ORCID: https://orcid.org/0000-0002-3393-1472)
- Zhanbing Ma
Institutions
- Shanghai Jiao Tong University (CN)
- Ningxia Medical University (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1186/s12889-025-22419-7
- Citations
- 10
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 29.38
Funders
- National Natural Science Foundation of China