Predicting VBAC Outcomes from U.S. Natality Data using Deep and Classical Machine Learning Models

TL;DR

This study develops machine learning models to predict vaginal birth after cesarean (VBAC) using U.S. natality data, demonstrating moderate predictive performance and identifying key maternal factors influencing outcomes.

Contribution

Introduces and compares deep and classical machine learning models for VBAC prediction using a large national dataset, highlighting their potential for clinical decision support.

Findings

ML models achieved AUC up to 0.729 for VBAC prediction.

Key predictors include maternal BMI, education, parity, and comorbidities.

Models outperform baseline logistic regression, supporting early-pregnancy data use.

Abstract

Accurately predicting the outcome of a trial of labor after cesarean (TOLAC) is essential for guiding prenatal counseling and minimizing delivery-related risks. This study presents supervised machine learning models for predicting vaginal birth after cesarean (VBAC) using 643,029 TOLAC cases from the CDC WONDER Natality dataset (2017-2023). After filtering for singleton births with one or two prior cesareans and complete data across 47 prenatal-period features, three classifiers were trained: logistic regression, XGBoost, and a multilayer perceptron (MLP). The MLP achieved the highest performance with an AUC of 0.7287, followed closely by XGBoost (AUC = 0.727), both surpassing the logistic regression baseline (AUC = 0.709). To address class imbalance, class weighting was applied to the MLP, and a custom loss function was implemented in XGBoost. Evaluation metrics included ROC curves, confusion matrices, and precision-recall analysis. Logistic regression coefficients highlighted maternal BMI, education, parity, comorbidities, and prenatal care indicators as key predictors. Overall, the results demonstrate that routinely collected, early-pregnancy variables can support scalable and moderately high-performing VBAC prediction models. These models offer potential utility in clinical decision support, particularly in settings lacking access to specialized intrapartum data.