Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes

doi:10.4239/wjd.122555

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 17, Issue 9

This Article

(0)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Diabetes

ISSN

1948-9358

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

- Full Article (XML)

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Diabetes. Sep 15, 2026; 17(9): 122555
Published online Sep 15, 2026. doi: 10.4239/wjd.122555

Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes

Zhi-Yuan Fan, Xian-Hui Ran, Na Wang, Tian-Yi Zhao, Hui Li, Xiao Liu, Jin Wu, Zhen Yang, Gang Chen, Lei Yang, Xiao Ma

Zhi-Yuan Fan, Xian-Hui Ran, Na Wang, Tian-Yi Zhao, Hui Li, Jin Wu, Zhen Yang, Gang Chen, Xiao Ma, Health Checkup Center, China-Japan Friendship Hospital, Beijing 100029, China

Xiao Liu, Department of Pharmacy, China-Japan Friendship Hospital, Beijing 100029, China

Lei Yang, Health Checkup Center, Henan Honliv Hospital, Xinxiang 453499, Henan Province, China

Xiao Ma, State Key Laboratory of Respiratory Health and Multimorbidity, China-Japan Friendship Hospital, Beijing 100029, China

Co-first authors: Zhi-Yuan Fan and Xian-Hui Ran.

Co-corresponding authors: Lei Yang and Xiao Ma.

Author contributions: Fan ZY and Ran XH contributed equally to this study, including study design, data analysis, and manuscript preparation, wrote the original manuscript and revised the paper as co-first authors; Fan ZY, Ran XH, Wang N, Zhao TY, Li H, Liu X, Wu J, Yang Z, Chen G, and Yang L were responsible for data curation, methodology, and participated in formal analysis; Yang L and Ma X were the guarantor of the study and were responsible for conceptualization, project administration, supervision, methodology, writing review and editing as co-corresponding authors; all authors have read and approved the final manuscript.

AI contribution statement: Portions of this manuscript were edited using AI tools solely for language refinement. The authors carefully reviewed and verified all AI-assisted outputs and take full responsibility for the scientific content of the manuscript.

Supported by 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; and National High Level Hospital Clinical Research Funding, No. 2025-NHLHCRF-PY-13.

Institutional review board statement: The study was approved by the Ethics Committee of China-Japan Friendship Hospital (No. 2025-KY-353).

Informed consent statement: The ethics committee agrees to waive informed consent.

Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.

STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.

Data sharing statement: De-identified data in this study may be provided to qualified researchers upon reasonable request.

Corresponding author: Xiao Ma, MD, PhD, Dean, Health Checkup Center, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China. maxiaocjfh@163.com

Received: April 23, 2026
Revised: June 11, 2026
Accepted: July 15, 2026
Published online: September 15, 2026
Processing time: 131 Days and 5.8 Hours

Abstract

BACKGROUND

The rising prevalence of prediabetes is a major public health concern because of the substantial risk of progression to type 2 diabetes mellitus (T2DM). However, identifying individuals at high risk remains challenging due to the lack of reliable risk-stratification tools.

AIM

To develop a machine learning (ML)-based model to predict T2DM risk among individuals with prediabetes using routine health checkup data and to construct an interactive platform to support risk stratification.

METHODS

In this retrospective cohort study, individuals with prediabetes at baseline from two medical centers in China were included, with incident T2DM during follow-up defined as the study outcome. The development dataset comprised a training cohort (2012-2018) and a temporal testing cohort (2019-2021) from Beijing. An external validation cohort (2019-2021) from Henan Province was used to evaluate model generalizability. Four ML models were constructed to predict T2DM risk. Model performance was evaluated using the concordance index. Kaplan-Meier analysis was used to assess the risk-stratification ability of the best-performing model. Model interpretability was examined using Shapley Additive exPlanations.

RESULTS

A total of 27609 individuals with prediabetes were included. The gradient boosting survival analysis model showed the best predictive performance, with a concordance index of 0.813 (95%CI: 0.793-0.832) in the testing cohort and 0.759 (95%CI: 0.731-0.787) in the external validation cohort. The model effectively discriminated between high-risk and low-risk groups, which showed significantly different cumulative incidences of T2DM (P < 0.001). Shapley Additive exPlanations analysis identified fasting blood glucose, age, body mass index, monocyte count, and high-density lipoprotein cholesterol as the leading predictors.

CONCLUSION

An interpretable ML-based model using routine health checkup data demonstrated favorable performance for estimating T2DM risk among individuals with prediabetes and may support risk stratification in preventive care settings. Further prospective validation is warranted before broader implementation.

Keywords: 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.