Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study

Abstract

Aim: In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluated its prediction ability, and compared the model with the traditional logical regression model.

Methods: We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling.

Results: The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly (P < 0.05). The area under the receiver operating characteristic curve (AUC) score and the F1 score with the training set of the XGBoost model (0.877 ± 0.014 and 0.730 ± 0.019, respectively) were significantly higher than those of the logistic model (0.713 ± 0.013 and 0.568 ± 0.026, respectively). In the test set, the AUC and F1 scores of the XGBoost model (0.759 ± 0.023 and 0.566 ± 0.042, respectively) were also higher than those of the logistic model (0.695 ± 0.030 and 0.550 ± 049, respectively).

Conclusions: We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model.

Keywords: IVF-ET; Machine Learning; Missed abortion; Prediction model; XGBoost.

Figure 1. The ROC curves of the two models.

(A) The AUC of the logistic model vs. the AUC of the XGBoost model (0.713 vs 0.877) in the training set. (B) The AUC of the logistic model vs. the AUC of the XGBoost model (0.695 vs 0.759) in the test set.

Figure 2. Feature importance of the variables in the XGBoost model.

The most important influencing factor was activated partial thromboplastin time (APTT), followed by prolactin (PRL), anti-Müllerian hormone (AMH), female age, male age, abnormal ovarian structure, thyroid peroxidase antibody (TPO-Ab), and anticardiolipin antibody (ACA).