Implementation of machine learning models for the prediction of vaginal birth after cesarean delivery.

Raanan MeyerNatav HendinMichal ZamirNizan MorGabriel LevinEyal SivanDvir AranAbraham Tsur

Published in: 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 (2020)

All ML models performed significantly better than the MFMU-C. In the XGBoost model, eight variables were sufficient for accurate prediction. Prior arrest of descent and maternal height contribute to prediction more than prior arrest of dilation and maternal weight.

Keyphrases

machine learning
body mass index
pregnancy outcomes
birth weight
cell cycle
healthcare
primary care
physical activity
weight gain
weight loss
artificial intelligence
cell proliferation