ArticleEuropean journal of medical research2025
Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2026Article
- Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2026Article
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundFresh 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.
methodsA 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.
resultsMale 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.
conclusionsAn XGBoost-based model effectively predicts clinical pregnancy in EM patients after fresh embryo transfer, showing acceptable performance and interpretability.
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