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Related Experiment Video

Updated: Sep 10, 2025

External Cephalic Version: Is it an Effective and Safe Procedure?

External Cephalic Version: Is it an Effective and Safe Procedure?

Published on: June 6, 2020

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

Ming Yang^1,2, Dajian Long^1,2, Yunxiu Li³

¹Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.

The Journal of Maternal-Fetal & Neonatal Medicine : the Official Journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians

|August 25, 2025

Summary

This summary is machine-generated.

Machine learning models can predict successful vaginal birth after cesarean (VBAC). The CatBoost model demonstrated the best performance, identifying cervical Bishop score and interpregnancy interval as key predictors for VBAC success.

Keywords:

CatBoost Predictive models SHARP machine learning vaginal birth after cesarean section

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Last Updated: Sep 10, 2025

External Cephalic Version: Is it an Effective and Safe Procedure?

External Cephalic Version: Is it an Effective and Safe Procedure?

Published on: June 6, 2020

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Published on: January 27, 2010

Mouse Model of Surgical Uterine Injury and Subsequent Pregnancy Outcomes

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Published on: June 27, 2025

Area of Science:

Obstetrics and Gynecology
Medical Informatics
Machine Learning in Healthcare

Background:

Vaginal birth after cesarean (VBAC) is recommended, but predicting success remains challenging.
Existing tools lack precision in identifying eligible candidates for VBAC.
Machine learning (ML) offers potential for developing accurate predictive models in obstetrics.

Purpose of the Study:

To develop an explainable machine learning (ML) model for predicting the likelihood of successful VBAC.
To identify key factors influencing VBAC success using ML interpretability techniques.

Main Methods:

Analysis of 2438 women undergoing trial of labor after cesarean (TOLAC) from two Chinese tertiary hospitals.
Development and evaluation of seven ML-based predictive models using AUC.
Selection of the optimal model (CatBoost) and interpretation of its predictions using SHAP values.

Main Results:

The CatBoost model achieved the highest AUC of 0.767, with an accuracy of 0.652.
SHAP analysis revealed that cervical Bishop score and interpregnancy interval were the most influential factors for successful VBAC.
The model demonstrated good performance in predicting VBAC outcomes.

Conclusions:

ML models, particularly the CatBoost model, can effectively predict VBAC success.
Clinicians should utilize these models for systematic benefit-risk analysis and individualized patient assessment.
Further research can refine ML-based tools for enhanced VBAC counseling and decision-making.