Development and internal validation of machine learning models for predicting prediabetes in China
- 1Department of Physical Education, Qinghai University, Xining, People’s Republic of China
- 2Medical College, Qinghai University, Xining, People’s Republic of China
Abstract
Background: Diabetes imposes a growing health burden among Chinese adults, necessitating scalable tools for early identification of high-risk individuals. We aimed to develop and internally validate survival machine-learning models for prediabetes risk stratification using routinely available clinical indicators. Methods: This retrospective cohort study included 77,478 Chinese adults from the Dryad repository (2010–2016). Candidate predictors were selected via LASSO regression and random survival forests (RSF). Six survival models, Cox proportional hazards (Cox-PH), CoxBoost, DeepSurv, gradient boosting machine (GBM), random survival forests (RSF), and survival tree, were developed and evaluated using the concordance index (C-index), time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). The best-performing model was deployed as a web-based application. Results: Eight predictors were consistently selected across methods: age, BMI, fasting plasma glucose, total cholesterol, triglycerides, HDL-C, LDL-C, and creatinine clearance rate. In the test set, DeepSurv and GBM achieved comparable discrimination (C-index: 0.756 vs. 0.751; adjusted P = 0.694). GBM was preferred due to better calibration and greater net benefit across clinically relevant thresholds, leading to its selection as the candidate model. Conclusion: The GBM-based prediabetes risk model, built on eight inexpensive and routinely available clinical measures, showed promising internal discrimination and calibration in a large Chinese cohort. An interactive web tool was developed as a research prototype to facilitate future external validation and early risk identification. External validation in independent populations is warranted before clinical use.
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BibTeX
@article{jiang2026development,
title = {{Development and internal validation of machine learning models for predicting prediabetes in China}},
author = {Zhengxin Jiang and Wen Qian and Haifang Yang and Yifang Luo and Wen Peng and Qiong Wu and Xiaoxing Wei},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0361704},
url = {https://doi.org/10.1371/journal.pone.0361704},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and internal validation of machine learning models for predicting prediabetes in China
AU - Zhengxin Jiang
AU - Wen Qian
AU - Haifang Yang
AU - Yifang Luo
AU - Wen Peng
AU - Qiong Wu
AU - Xiaoxing Wei
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0361704
UR - https://doi.org/10.1371/journal.pone.0361704
AB - Background: Diabetes imposes a growing health burden among Chinese adults, necessitating scalable tools for early identification of high-risk individuals. We aimed to develop and internally validate survival machine-learning models for prediabetes risk stratification using routinely available clinical indicators. Methods: This retrospective cohort study included 77,478 Chinese adults from the Dryad repository (2010–2016). Candidate predictors were selected via LASSO regression and random survival forests (RSF). Six survival models, Cox proportional hazards (Cox-PH), CoxBoost, DeepSurv, gradient boosting machine (GBM), random survival forests (RSF), and survival tree, were developed and evaluated using the concordance index (C-index), time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). The best-performing model was deployed as a web-based application. Results: Eight predictors were consistently selected across methods: age, BMI, fasting plasma glucose, total cholesterol, triglycerides, HDL-C, LDL-C, and creatinine clearance rate. In the test set, DeepSurv and GBM achieved comparable discrimination (C-index: 0.756 vs. 0.751; adjusted P = 0.694). GBM was preferred due to better calibration and greater net benefit across clinically relevant thresholds, leading to its selection as the candidate model. Conclusion: The GBM-based prediabetes risk model, built on eight inexpensive and routinely available clinical measures, showed promising internal discrimination and calibration in a large Chinese cohort. An interactive web tool was developed as a research prototype to facilitate future external validation and early risk identification. External validation in independent populations is warranted before clinical use.
SN - 1932-6203
ER -