Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.

Objective: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings.

Methods: We conducted a retrospective multicenter cohort study in Northeast China, including women who had vaginal deliveries at three tertiary hospitals from September 2018 to December 2023. Data were extracted from electronic medical records. The dataset was split into a training set (70%) and an internal validation set (30%) to prevent overfitting. External validation was performed on a separate dataset. Several evaluation metrics, including the area under the receiver operating characteristic curve (AUC), were used to compare prediction performance. Features were ranked using SHAP, and the final model was explained.

Results: The XGBoost model demonstrated superior predictive accuracy for PPH, with an AUC of 0.997 in the training set. SHAP value-based feature selection identified 15 key features contributing to the model's predictive power. SHAP dependence and summary plots provided intuitive insights into each feature's contribution, enabling the identification of anomalies. The final model maintained high predictive power, with an AUC of 0.894 in internal validation and 0.880 in external validation.

Conclusion: This study successfully developed an interpretable ML model that predicts PPH with high accuracy. Future studies with larger and more diverse datasets are necessary to further validate and refine the model, particularly to assess its generalizability across different populations and healthcare settings.