Advanced Machine Learning did not Surpass Traditional Logistic Regression in First-Trimester Gestational Diabetes Mellitus Prediction: A Retrospective Single-Center Study From Eastern China.

Background: Gestational diabetes mellitus (GDM) poses serious health risks to both mothers and fetuses. However, effective tools for identifying GDM are lacking. This study, based on a Chinese cohort, aims to construct and compare the predictive performance of traditional logistic regression (LR) and six advanced machine learning (ML) models, thereby aiding in the early identification and intervention of GDM. Methods: This retrospective study utilized medical examination data from 956 singleton pregnant women collected between January and December 2023 from ten maternal and child health hospitals in Pinghu City. We employed receiver operating characteristic curves and precision-recall curves to assess the predictive performance of the models. Decision curve analysis (DCA) was used to evaluate clinical utility, while calibration curves and Hosmer-Lemeshow (HL) tests were applied to assess the calibration of each model. Results: The 956 participants were randomly divided into a training set and a validation set at a 3:1 ratio. We identified 13 features through Spearman correlation analysis and the Boruta algorithm to construct the models. The LR model exhibited the best AUC at 0.787 (0.723-0.85), outperforming the seven other ML models including RF at 0.776 (0.711-0.841). Furthermore, the LR model showed good calibration and clinical utility. Conclusion: Although ML has tremendous potential, in predicting the occurrence of GDM based on common early pregnancy data, the ML models did not completely outperform the traditional LR model. Simpler, traditional models may be more effective than complex ML approaches.