March 3, 2026Open Access

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

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.

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

Key Points

Abstract

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