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

Key Points

• CPSPP treatment outcomes can be predicted using machine learning algorithms, enhancing clinical decision-making.
• The SHAP analysis identifies critical risk factors for CPSPP, aiding in understanding patient-specific needs.
• This observational analysis highlights the significance of a machine learning prediction model in healthcare settings.
• The findings call for further exploration of personalized treatment strategies based on identified risk factors.

Abstract