ArticleEuropean journal of medical research2026
Machine learning-based prediction model for chronic post-surgical pelvic pain syndrome: a comprehensive analysis using SHAP interpretability.
Article in European journal of medical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundChronic post-surgical pelvic pain syndrome (CPSPP) represents a significant clinical challenge affecting a substantial proportion of patients undergoing pelvic surgical procedures. The complex pathophysiology and multifactorial nature of CPSPP necessitate advanced predictive approaches to improve patient outcomes and optimize treatment strategies.
objectiveThis study aimed to develop and validate a machine learning-based prediction model for CPSPP using comprehensive clinical data, with particular emphasis on model interpretability through SHAP (SHapley Additive exPlanations) analysis to identify key risk factors and enhance clinical decision-making.
methodsA retrospective cohort study was conducted involving 62 patients who underwent pelvic surgical procedures. Comprehensive clinical data including demographic characteristics, surgical parameters, pain assessments, and postoperative outcomes were collected. Multiple machine learning algorithms were employed to develop predictive models, with performance evaluation using receiver operating characteristic (ROC) analysis. SHAP values were utilized to provide model interpretability and identify the most influential predictive features.
resultsA total of 62 female patients were analyzed, with a mean age of 47.6 ± 11.8 years and BMI of 26.1 ± 4.9 kg/m
conclusionsThis 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.
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