1. Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study
- Author
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Guanghui Yuan, Bohan Lv, Xin Du, Huimin Zhang, Mingzi Zhao, Yingxue Liu, and Cuifang Hao
- Subjects
IVF-ET ,Missed abortion ,Machine Learning ,Prediction model ,XGBoost ,Medicine ,Biology (General) ,QH301-705.5 - 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
- Published
- 2023
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