Personalized machine learning model for predicting successful vaginal delivery during the active phase of labor

Accurate personalized prediction of delivery mode during the active stage improves resource allocation, reduces unnecessary interventions, and enhances patient-centered care. However, current machine learning models lack focus on active labor and effective use of labor-related data. The "black box" problem further impedes understanding, accuracy, and patient safety. Our aim was to develop a personalized model using real-time data to predict successful vaginal deliveries in the active phase and provide an explanation of the model's decision-making process STUDY DESIGN: Labor records from a tertiary hospital were searched and labeled over a 12-year period (2010-2021). Three models were trained using both "static features" (e.g demographics) and "dynamic features" (e.g cervical dilatation, efficacy, and newly derived features calculated as ratios between adjusted dynamic measures and time differences). To assess the explainability of the best-performing model, we employed SHAP (SHapley Additive exPlanations) analysis.

A total of 94,386 cases in which a trial of labor was attempted were identified. Of them 70,000 admitted to the active phase of labor (6 cm dilatation). We trained three types of classifier models predicting successful vaginal delivery: Random Forest classifier, Histogram-Based Gradient Boosting classifier, and neural network model. Histogram-Based Gradient Boosting model achieved the highest scores: ROC AUC score and Precision recall AUC score was 0.87 (95% confidence interval, 0.858-0.892) and 0.52 (95% confidence interval, 0.511-0.542) respectively. Explainability of the best model was analyzed using SHAP. The features that contributed the most for the model prediction were: rate of fetal descent, cervical opening rate, maternal age and clinical fetal weight estimation.

This study presents a personalized, explainable machine learning model that continuously predicts delivery mode in real time during active labor. By integrating dynamic intrapartum data with static maternal characteristics, the model provides interpretable, patient-specific risk estimates that can enhance intrapartum decision-making and optimize obstetric care.