Publication Name	World Journal of Diabetes
Manuscript ID	122555
DOI	10.4239/wjd.122555
Country	China

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
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.
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; In press DOI: https://doi.org/10.4239/wjd.122555
PDF	122555-in-press.pdf

Received	2026-04-23 05:49
Peer-Review Started	2026-04-26 23:53
First Decision by Editorial Office Director	2026-05-27 01:33
Return for Revision	2026-05-27 01:33
Revised	2026-06-11 06:51
Publication Fee Transferred	2026-06-14 08:58
Second Decision by Editor	2026-07-08 02:40
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-07-15 07:03
Articles in Press	2026-07-15 07:03
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9358 (online)
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