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

• URL: http://arxiv.org/abs/2507.21330v1
• Date: Mon, 28 Jul 2025 20:54:55 GMT
• Title: Predicting VBAC Outcomes from U.S. Natality Data using Deep and Classical Machine Learning Models
• Authors: Ananya Anand,
• Abstract summary: This study presents supervised machine learning models for predicting vaginal birth after cesarean (VBAC) from the CDC WONDER Natality dataset.<n>Three classifiers were trained: logistic regression, XGBoost, and a multilayer perceptron (MLP)<n>The models achieved the highest performance with an AUC of 0.7287, followed closely by XGBoost (Boost = 0.727), both surpassing the logistic regression baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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