| Category |
Public, Environmental & Occupational Health |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Zhi-Yuan Fan, Xian-Hui Ran, Na Wang, Tian-Yi Zhao, Hui Li, Xiao Liu, Jin Wu, Zhen Yang, Gang Chen, Lei Yang and Xiao Ma |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National High Level Hospital Clinical Research Funding, Elite Medical Professionals Initiative of China-Japan Friendship Hospital |
No. ZRJY2025-QMPY41 |
| Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences |
No. 2022-ZHCH330-01 |
| National High Level Hospital Clinical Research Funding |
No. 2023-NHLHCRF-YXHZ-ZRMS-06 |
| National High Level Hospital Clinical Research Funding |
No. 2025-NHLHCRF-PY-13 |
|
| Corresponding Author |
Xiao Ma, Dean, MD, PhD, Health Checkup Center, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China. maxiaocjfh@163.com |
| Key Words |
Prediabetes; Type 2 diabetes mellitus; Health checkup; Machine learning; Risk prediction |
| Core Tip |
We developed and externally validated an interpretable machine learning model to predict progression from prediabetes to type 2 diabetes mellitus using routine health checkup data. In a multicenter Chinese cohort, gradient boosting survival analysis demonstrated favorable discrimination (concordance index: 0.813 in temporal validation and 0.759 in external validation) and effectively stratified high-risk individuals. Shapley Additive exPlanations improved model transparency by identifying key predictors, including fasting blood glucose, age and body mass index. An online risk calculator was developed to support individualized risk assessment in preventive care settings. |
| Publish Date |
2026-09-04 05:57 |
| Citation |
Fan ZY, Ran XH, Wang N, Zhao TY, Li H, Liu X, Wu J, Yang Z, Chen G, Yang L, Ma X. Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes. World J Diabetes 2026; 17(9): 122555
|
| URL |
https://www.wjgnet.com/1948-9358/full/v17/i9/122555.htm |
| DOI |
https://doi.org/10.4239/wjd.122555 |
