Machine learning-based prediction model for chronic post-surgical pelvic pain syndrome: a comprehensive analysis using SHAP interpretability

This study presents a novel machine learning approach for predicting CPSPP treatment outcomes with enhanced interpretability through SHAP analysis. The findings contribute to improved understanding of CPSPP risk factors and provide a foundation for personalized treatment strategies and clinical decision support systems.