Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.

ABSTRACT:

Background

Infertility affects approximately 15% of couples globally, with assisted reproductive technologies (ARTs) becoming the primary interventions. Despite the growing use of ARTs, success rates have plateaued at around 30%, highlighting the need for improved predictive models to enhance outcomes. This study aimed to develop a machine learning-based predictive model for live birth outcomes following fresh embryo transfer.

Methods

A total of 51,047 ART records were collected from 2016 to 2023 at the Shanghai First Maternity and Infant Hospital. After data preprocessing, 11,728 records and 55 pre-pregnancy features were analyzed. Six machine learning models-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machines (GBM), Adaptive Boosting (AdaBoo

SUBMITTER: Wu S 

PROVIDER: S-EPMC12462326 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature