Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.

Abstract

Objective: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcomes and models with higher accuracy were identified for potential implementation in clinical settings.

Methods: The clinical data and pregnancy outcomes of 2625 women who underwent fresh cycles of IVF-ET between 2016 and 2022 at the Reproductive Center of the Affiliated Hospital of Inner Mongolia Medical University were enrolled to establish a comprehensive dataset. The observed features were preprocessed and analyzed. A predictive model for pregnancy outcomes of IVF-ET treatment was constructed based on the processed data. The dataset was divided into a training set and a test set in an 8:2 ratio. Predictive models for clinical pregnancy and clinical live births were developed. The ROC curve was plotted, and the AUC was calculated and the prediction model with the highest accuracy rate was selected from multiple models. The key features and main aspects of IVF-ET treatment outcome prediction were further analyzed.

Results: The clinical pregnancy outcome was categorized into pregnancy and live birth. The XGBoost model exhibited the highest AUC for predicting pregnancy, achieving a validated AUC of 0.999 (95% CI: 0.999-1.000). For predicting live births, the LightGBM model exhibited the highest AUC of 0.913 (95% CI: 0.895-0.930).

Conclusion: The XGBoost model predicted the possibility of pregnancy with an accuracy of up to 0.999. While the LightGBM model predicted the possibility of live birth with an accuracy of up to 0.913.