Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.

ABSTRACT:

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)-we

SUBMITTER: Wan X 

PROVIDER: S-EPMC12406419 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature