Supplementary file 1_Development and internal temporal validation of an explainable XGBoost model for predicting clinical pregnancy after frozen embryo transfer.docx

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.</p>Research question<p>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?</p>Design<p>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).