Interpretable machine learning for predicting placental abruption in early-onset preeclampsia: model development and evaluation

The SHAP-based RF model demonstrated high predictive accuracy and interpretability, providing a transparent, data-driven framework for individualized risk assessment of placental abruption in EOPE. This interpretable approach may facilitate early risk identification and personalized management in clinical practice.