Development and validation of a machine learning-based predictive model for live birth outcomes following fresh embryo transfer in patients with endometriosis

Abstract

Objective: This study aims to develop a machine learning-based predictive model for patients with endometriosis, with the goal of precisely identifying key factors and reliable predictive markers that influence live birth outcomes following fresh embryo transfer. Through systematic evaluation of multiple algorithms, efforts will be made to identify the optimal model for elucidating high-risk factors affecting live birth, thereby providing a basis for formulating targeted interventions to enhance the live birth rate in this population undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).

Methods: This study adopted a retrospective cohort design and included 1836 patients with endometriosis who underwent fresh embryo transfer via in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) at Fujian Provincial Maternity and Children's Hospital between 2018 and 2023. Participants were randomly allocated to either the training set or the validation set, with a 70:30 split (1285 in the training set and 551 in the validation set), making this an internal validation study. Independent variables were screened using the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RFE) algorithms. For eight machine learning models, namely decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), light gradient boosting machine (LightGBM), naive Bayes model (NBM), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), we determined the optimal hyperparameter configurations using the grid search strategy. All models were trained, and their performances were evaluated through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Brier score (BS). The results showed that the XGBoost model exhibited the best predictive performance and was thus selected as the final modeling solution. In addition, the feature importance analysis combined with the SHapley Additive exPlanations (SHAP) value dependency plots systematically revealed the relative contributions and influence mechanisms of key features on the model predictions.

Results: Lasso and RFE analyses identified eight predictive variables for model development. The AUC values for DT, KNN, LightGBM, LR, naive Bayes, RF, SVM, and XGBoost in the training set were 0.784, 0.987, 0.841, 0.800, 0.803, 0.988, 0.799, and 0.920, while those in the test set were 0.765, 0.748, 0.801, 0.805, 0.810, 0.820, 0.807, and 0.852, respectively. XGBoost demonstrated the highest predictive performance among all models. SHAP analysis identified anti-Mullerian hormone (AMH), female age, antral follicle count (AFC), infertility duration, GnRH agonist protocol, revised American Fertility Society (rAFS) stage, normal fertilization number, and number of transferred embryos as key predictors for live birth following fresh embryo transfer in patients with endometriosis.

Conclusion: This study developed a machine learning-based predictive model for live birth following fresh embryo transfer in patients with endometriosis and systematically evaluated the comparative performance of multiple algorithms. The XGBoost model demonstrated superior overall performance, facilitating timely and precise identification of high-risk factors influencing live birth outcomes. These findings can inform targeted interventions to improve pregnancy outcomes in women with endometriosis.

Keywords: Endometriosis; Fresh embryo transfer; In vitro fertilization-embryo transfer; Live birth; Machine learning.