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

التفاصيل البيبلوغرافية
العنوان: Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study
المؤلفون: Guanghui Yuan, Bohan Lv, Xin Du, Huimin Zhang, Mingzi Zhao, Yingxue Liu, Cuifang Hao
المصدر: PeerJ, Vol 11, p e14762 (2023)
بيانات النشر: PeerJ Inc., 2023.
سنة النشر: 2023
المجموعة: LCC:Medicine
LCC:Biology (General)
مصطلحات موضوعية: IVF-ET, Missed abortion, Machine Learning, Prediction model, XGBoost, Medicine, Biology (General), QH301-705.5
الوصف: 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
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2167-8359
Relation: https://peerj.com/articles/14762.pdf; https://peerj.com/articles/14762/; https://doaj.org/toc/2167-8359
DOI: 10.7717/peerj.14762
URL الوصول: https://doaj.org/article/2d58c9996dc3435880c79fdb76ad2d04
رقم الأكسشن: edsdoj.2d58c9996dc3435880c79fdb76ad2d04
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21678359
DOI:10.7717/peerj.14762