Predicting postpartum glucose intolerance in women with gestational diabetes mellitus in primary care: A machine learning approach using XGBoost and SHAP values.

OBJECTIVE(S): To develop and internally validate an interpretable machine learning model using eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) to predict postpartum glucose intolerance among women with GDM using routine antenatal clinical data.

STUDY

DESIGN

This retrospective study included 600 women with GDM who completed a 6-week postpartum 75-g OGTT. Forty-three antenatal variables were extracted from electronic medical records. An XGBoost model was trained using stratified 5-fold cross-validation, ROSE oversampling, and gridsearch optimisation. Model performance was evaluated using AUC, precision, recall, F1-score and negative predictive value (NPV). SHAP analysis was used to assess feature importance and interpretability.

RESULTS

Postpartum glucose intolerance occurred in 19% of participants. The XGBoost model achieved an AUC of 0.671 and PR-AUC of 0.35, with precision of 0.79, recall of 0.82 and an NPV of 0.87. SHAP analysis identified fasting plasma glucose, 2-hour glucose, gestational weight gain, multiparity, previous GDM and family history of diabetes as key predictors.

CONCLUSION

An interpretable XGBoost model with SHAP explanations using routine antenatal data shows promise for postpartum glucose risk assessment in primary care. Despite moderate predictive performance, the model demonstrated a high negative predictive value.