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

Accurate prediction of vaginal birth after cesarean is crucial for selecting women suitable for a trial of labor after cesarean (TOLAC). We sought to develop a machine learning (ML) model for prediction of TOLAC success and to compare its accuracy with that of the MFMU model. All consecutive singleton TOLAC deliveries from a tertiary academic medical center between February 2017 and December 2018 were included. We developed models using the following ML algorithms: random forest (RF), regularized regression (GLM), and eXtreme gradient-boosted decision trees (XGBoost). For developing the ML models, we disaggregated BMI into height and weight. Similarly, we disaggregated prior arrest of progression into prior arrest of dilatation and prior arrest of descent. We applied a nested cross-validation approach, using 100 random splits of the data to training (80%, 792 samples) and testing sets (20%, 197 samples). We used the area under the precision-recall curve (AUC-PR) as a measure of accuracy. Nine hundred and eighty-nine TOLAC deliveries were included in the analysis with an observed TOLAC success rate of 85.6%. The AUC-PR in the RF, XGBoost and GLM models were 0.351 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.