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 gradually used to develop predictive models in obstetrics and midwifery owing to its excellent performance. This study aimed to develop an explainable ML model to predict the chance of successful VBAC.