Evaluation of machine learning and logistic regression-based gestational diabetes prognostic models

Despite significant changes in the background characteristics of the population, we have demonstrated that all models remained robust, especially after recalibration. However, the performance of the original ML model decreased significantly during validation. Dynamic models are better suited to adapt to the temporal changes in baseline characteristics of pregnant women and the resulting calibration drift, as they can incorporate new data without requiring manual evaluation.