Development and internal validation of machine learning models for predicting prediabetes in China

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.