Risk factors for early miscarriage in patients with thin endometrium receiving in vitro fertilization/intracytoplasmic sperm injection-embryo transfer：a study based on machine learning-based predictive modeling

Abstract:

Objective To investigate the influencing factors for early miscarriage in in patients with thin endometrium during fresh embryo transfer based on multiple machine learning methods，to establish a predictive model，and to provide reasonable ideas for preventing early miscarriage in patients with thin endometrium undergoing fresh embryo transfer.Methods A total of 1153 patients with thin endometrium who underwent fresh embryo transfer for the first time were enrolled in this study，and LASSO regression and random forest recursive feature elimination（RFE） were used for feature selection. Six machine learning models were developed and compared in terms of cross validation，accuracy，sensitivity，recall rate，f1 value，area under the ROC curve，and calibration curve. SHAP plots were used to elucidate the influencing factors for early miscarriage.Results A total of 29 feature variables were identified by LASSO regression and random forest RFE and were included in the six machine learning models，among which the multilayer perceptron model showed the best discriminatory ability for early miscarriage，with an area under the ROC curve of 0.803（95%CI=0.772-0.834）. The random forest，XGBoost，and AdaBoost models had an area under the ROC curve of >0.7.Conclusion This study establishes a machine learning-based predictive model for early miscarriage in patients with thin endometrium during fresh embryo transfer，and validation of various evaluation metrics shows that the model has good performance and can help clinicians to achieve the early diagnosis of patients，thereby providing ideas for improving the pregnancy outcome of patients at high risk of early miscarriage in the future.