Predicting ovarian responses to the controlled ovarian hyperstimulation in elderly infertile women using clinical measurements and random forest regression

Abstract

During the past decades, the number of elderly infertile women is obviously increasing in China, and more and more of them are likely to seek medical assisted reproductive technologies. As the in vitro fertilization/embryo transfer (IVF/ET) treatment presents special medical and psychological challenges to elderly infertile women, it is extremely helpful to perform the clinical evaluation and outcome prediction regarding IVF/ET outcomes. In this study, we retrospectively collected 12 clinical measurements in prior to the oocyte recovery for 689 elderly infertile patients (≥35 years of old), and used for predicting ovarian responses to the controlled ovarian hyperstimulation based on random forest regression models. Using different predictor sets and 10-fold cross validation approach, the Mean Square Error (±standard deviation) of prediction models varied from 7.56 ± 0.31 to 13.90 ± 0.37 in the training datasets, and the correlation coefficients between observed and predicted values ranged from 0.86 ± 0.02 to 0.72 ± 0.05 in the testing datasets. Among all clinical measurements involved in this study, the preovulatory follicle count (PFC), antral follicle count (AFC), and anti-Müllerian hormone (AMH) were revealed to be the most important features in prediction models. In conclusion, we successfully established the machine learning approach that could help the elderly infertile patients to better understand the most possible outcomes in subjecting to the controlled ovarian hyperstimulation.