Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method

TLDR

Infertility is a growing global health concern, with increasing numbers of couples seeking IVF, yet many remain childless after multiple cycles and face higher risks and financial burdens. The study aimed to develop a model that predicts the live‑birth probability before a patient’s first IVF treatment to aid counseling and expectation setting. Retrospective data from 7,188 women’s first IVF cycles were used to train machine‑learning models on 70 % of the cohort, validate on 30 % with ROC and calibration plots, and assess generalization via nested cross‑validation. The XGBoost model achieved an AUC of 0.73 and an average accuracy of 0.70 ± 0.003, outperforming other algorithms and providing a calibrated estimate of live‑birth chance.

Abstract

Abstract Background Infertility has become a global health issue with the number of couples seeking in vitro fertilization (IVF) worldwide continuing to rise. Some couples remain childless after several IVF cycles. Women undergoing IVF face greater risks and financial burden. A prediction model to predict the live birth chance prior to the first IVF treatment is needed in clinical practice for patients counselling and shaping expectations. Methods Clinical data of 7188 women who underwent their first IVF treatment at the Reproductive Medical Center of Shengjing Hospital of China Medical University during 2014–2018 were retrospectively collected. Machine-learning based models were developed on 70% of the dataset using pre-treatment variables, and prediction performances were evaluated on the remaining 30% using receiver operating characteristic (ROC) analysis and calibration plot. Nested cross-validation was used to make an unbiased estimate of the generalization performance of the machine learning algorithms. Results The XGBoost model achieved an area under the ROC curve of 0.73 on the validation dataset and showed the best calibration compared with other machine learning algorithms. Nested cross-validation resulted in an average accuracy score of 0.70 ± 0.003 for the XGBoost model. Conclusions A prediction model based on XGBoost was developed using age, AMH, BMI, duration of infertility, previous live birth, previous miscarriage, previous abortion and type of infertility as predictors. This study might be a promising step to provide personalized estimates of the cumulative live birth chance of the first complete IVF cycle before treatment.