标题： 从美国出生数据预测VBAC结果，使用深度和经典机器学习模型 显示英文标题

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

摘要： 准确预测剖宫产后再试产（TOLAC）的结果对于指导产前咨询和降低分娩相关风险至关重要。 本研究展示了监督机器学习模型，使用来自CDC WONDER出生数据集（2017-2023年）的643,029例TOLAC案例来预测剖宫产后阴道分娩（VBAC）。 在筛选出单胎妊娠且有一次或两次既往剖宫产史，并且涵盖47个产前期特征的完整数据后，训练了三种分类器：逻辑回归、XGBoost和多层感知器（MLP）。 MLP表现出最高的性能，AUC为0.7287，紧随其后的是XGBoost（AUC = 0.727），两者均超过了逻辑回归基线（AUC = 0.709）。 为解决类别不平衡问题，对MLP应用了类别权重，并在XGBoost中实现了自定义损失函数。 评估指标包括ROC曲线、混淆矩阵和精确率-召回率分析。 逻辑回归系数突出了产妇BMI、教育程度、产次、合并症和产前护理指标作为关键预测因素。 总体而言，结果表明常规收集的早期妊娠变量可以支持可扩展且中等高性能的VBAC预测模型。 这些模型在临床决策支持中具有潜在的应用价值，尤其是在缺乏专门产时数据的环境中。 显示英文摘要

摘要： 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.