An explainable machine learning model in predicting vaginal birth after cesarean section

Objective Vaginal birth after cesarean section (VBAC) is recommended by obstetrical guidelines or expert consensuses. However, no valid tools can exactly predict who can have a vaginal birth among eligible candidates with one prior cesarean section. In recent years, machine learning (ML) is graduall...

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Bibliographic Details

Published in:	The Journal of Maternal-Fetal & Neonatal Medicine
Main Authors:	Ming Yang, Dajian Long, Yunxiu Li, Xiaozhu Liu, Zhi Bai, Zhongjun Li
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-12-01
Subjects:	Predictive models machine learning vaginal birth after cesarean section SHARP CatBoost
Online Access:	https://www.tandfonline.com/doi/10.1080/14767058.2025.2546544