European journal of medical research · 2025
Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.
СтатьяНазвание и аннотация: Перевод в очереди
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Wan Xiaoju, Yu Min, Wu Xingwu, Huang Zhihui, Tan Jun
Аннотация
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited application of machine learning (ML) approaches. This study aimed to develop an ML-based predictive model for clinical pregnancy in EM patients undergoing fresh embryo transfer.
METHODS: A retrospective analysis included 1752 EM patients undergoing IVF/ICSI with fresh embryo transfer (2014-2024). Twenty-four clinical and embryonic characteristics were predictors; clinical pregnancy was the outcome. Six ML models-Naïve Bayes, Logistic Regression, Random Forest, k-Nearest Neighbors, Neural Network, and eXtreme Gradient Boosting (XGBoost)-were developed and compared. Feature selection involved logistic regression and Random Forest recursive feature elimination, with tenfold cross-validation.
RESULTS: Male age (OR = 0.96, 95% CI 0.93-0.98, p < 0.001), normal fertilization count (OR = 1.07, 95% CI 1.03-1.11, p = 0.001), and transferred embryo count (OR = 1.61, 95% CI 1.24-2.08, p < 0.001) significantly predicted clinical pregnancy. The XGBoost model demonstrated optimal performance (training AUC: 0.764; testing AUC: 0.622). Shapley Additive Explanations (SHAP) provided model interpretability.
CONCLUSIONS: An XGBoost-based model effectively predicts clinical pregnancy in EM patients after fresh embryo transfer, showing acceptable performance and interpretability.
METHODS: A retrospective analysis included 1752 EM patients undergoing IVF/ICSI with fresh embryo transfer (2014-2024). Twenty-four clinical and embryonic characteristics were predictors; clinical pregnancy was the outcome. Six ML models-Naïve Bayes, Logistic Regression, Random Forest, k-Nearest Neighbors, Neural Network, and eXtreme Gradient Boosting (XGBoost)-were developed and compared. Feature selection involved logistic regression and Random Forest recursive feature elimination, with tenfold cross-validation.
RESULTS: Male age (OR = 0.96, 95% CI 0.93-0.98, p < 0.001), normal fertilization count (OR = 1.07, 95% CI 1.03-1.11, p = 0.001), and transferred embryo count (OR = 1.61, 95% CI 1.24-2.08, p < 0.001) significantly predicted clinical pregnancy. The XGBoost model demonstrated optimal performance (training AUC: 0.764; testing AUC: 0.622). Shapley Additive Explanations (SHAP) provided model interpretability.
CONCLUSIONS: An XGBoost-based model effectively predicts clinical pregnancy in EM patients after fresh embryo transfer, showing acceptable performance and interpretability.