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Open access Oct 2026

Development and validation of interpretable machine learning models for predicting cumulative live birth rates in assisted reproductive technology: a SHAP-based analysis

Objective To develop and validate interpretable machine-learning models predicting cumulative live birth rate (CLBR) in ART patients. Methods In this single-center retrospective cohort (January 2019–June 2024), 7,773 oocyte retrieval cycles were analysed. XGBoost gain ranking, applied to all screened cycles with native...

Xiao-Qian Fu, Yi-Si Qin, Qian-Yi Huang et al. · 0 citations

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