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Development and comparative evaluation of machine learning algorithms and Cox regression for predicting fasting
Su Hu1, Changshun Yan1, Xinyuan Tian2
1Department of Cardiology, the Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Objective:
To develop, compare, and externally evaluate machine learning (ML) and Cox regression models for predicting fasting plasma glucose (FPG)-defined incident prediabetes.
Methods:
We performed a secondary analysis of a publicly available Chinese health-examination cohort and conducted an external comparative evaluation in a separate hospital-based cohort. The development cohort of adults with normal baseline fasting glucose was divided into training and internal validation sets. Candidate predictors were selected using least absolute shrinkage and selection operator (LASSO) regression. Six ML models and an initial proportional-hazards Cox model were compared using discrimination and threshold-based metrics. After identifying FPG nonproportionality, four Cox specifications were compared. Model selection considered fit, performance in the internal validation set and external cohort, complexity, and parsimony. The final model was evaluated at the primary 3-, 4-, and 5-year horizons using time-dependent area under the receiver operating characteristic curve (AUC), Brier scores, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP), sensitivity analyses, and an online calculator supported interpretation and implementation.
Results:
Among 10, 592 participants, 1, 287 (12.2%) developed FPG-defined incident prediabetes over a mean follow-up of 3.05 years. LASSO retained 11 of 17 predictors. Random forest showed the highest apparent training performance, but its performance declined in the internal validation set and external cohort, whereas the initial Cox model demonstrated more consistent discrimination. FPG violated the proportional-hazards assumption. The extended Cox model incorporating FPG × log(t/3) (M2) had the lowest Akaike information criterion, accommodated the time-varying FPG effect, and performed comparably to more complex alternatives; it was therefore selected. M2 retained age, body mass index, diastolic blood pressure, FPG, and family history of diabetes. Its 3- and 4-year AUCs were 0.800 and 0.796 in training, 0.778 and 0.755 in internal validation, and 0.750 and 0.764 in the external cohort. Five-year performance was exploratory and less stable externally. SHAP identified FPG as the dominant contributor, and sensitivity analyses generally supported robustness.
Conclusion:
The final extended Cox model (M2) and online risk calculator may provide a practical framework for interpretable, individualized risk assessment of FPG-defined incident prediabetes, supporting early risk stratification and preventive risk-factor management using routinely available clinical indicators.
