Development and internal temporal validation of an explainable XGBoost model for predicting clinical pregnancy after frozen embryo transfer.

Key message: An explainable machine learning model was developed using comprehensive clinical variables to predict clinical pregnancy outcomes following frozen embryo transfer. Compared with conventional statistical models, the extreme gradient boosting-based model showed numerically higher discrimination and provided interpretable insights through SHapley Additive exPlanations (SHAP) analysis, supporting individualized decision-making in reproductive medicine. Research question: Can an interpretable XGBoost-based model leveraging multi-feature clinical data better predict pregnancy outcomes after frozen embryo transfer (FET) compared to conventional logistic regression? Design: This retrospective single-center study included 1,319 patients who underwent FET. Participants were temporally split into a training cohort (n=1,013) and a temporal validation cohort (n=306) according to the date of embryo transfer. Pre-transfer demographic, hormonal, treatment, and ultrasonographic variables were collected. An XGBoost model was developed and compared with logistic regression by assessing discrimination (area under the ROC curve [AUC] and area under the precision-recall curve [AUPRC]), calibration (calibration curves, Brier score, intercept, and slope), and clinical utility using decision curve analysis (DCA). Model interpretability was evaluated using SHapley Additive exPlanations (SHAP). Results: The XGBoost model achieved an AUC of 0.854 (95% CI 0.832-0.877) in training and 0.722 (95% CI 0.666-0.779) in temporal validation, with corresponding AUPRC values of 0.840 and 0.707. Calibration in the temporal validation cohort was acceptable (Brier score 0.212; calibration intercept 0.056; slope 0.965). DCA indicated a positive net benefit across clinically relevant thresholds, with high sensitivity at a representative probability threshold of 0.30. SHAP visualizations provided feature-level and individual-level explanations. Conclusion: Using single-center data, we developed and internally, temporally validated an explainable XGBoost model for predicting clinical pregnancy after FET. The model delivers accurate, interpretable predictions that may support personalized decision-making and facilitate the clinical integration of artificial intelligence into assisted reproductive medicine.