# Machine learning-based prediction of hernia risk in peritoneal dialysis patients: a comparative study of models and SHAP-driven interpretability analysis

## TL;DR

This study uses machine learning to predict hernia risk in peritoneal dialysis patients and provides an online tool for real-time risk assessment.

## Contribution

A high-accuracy hernia risk prediction model and an interpretable online tool for clinical use in peritoneal dialysis patients.

## Key findings

- The Random Forest model achieved high performance with training AUC of 97.99% and validation AUC of 93.66%.
- Nine core risk factors were identified, including age, BMI, and peritoneal transporter status, with SHAP analysis revealing non-linear effects.
- An R Shiny-based online tool was developed for real-time hernia risk calculation and clinical recommendations.

## Abstract

This study aimed to predict the hernia risk in peritoneal dialysis patients using machine learning (ML) models and conduct an interpretability analysis.

A total of 1,144 eligible PD patients (2010–2024) were divided into training (n = 800) and external validation (n = 344) cohorts. Nine ML models were constructed, and SHAP analysis was used for interpretability. Model performance was evaluated via AUC, accuracy, DCA, etc. An online visualization tool based on the optimal model was developed using R Shiny and deployed for clinical use.

The Random Forest (RF) model performed optimally (training AUC = 97.99%, validation AUC = 93.66%), identifying 9 core risk factors (age, BMI, PDV, albumin, smoking history, history of abdominal surgery, high peritoneal transporter status, COPD, and CAPD modality). SHAP clarified non-linear effects of these factors. The developed R Shiny-based online tool (https://caoyugang.shinyapps.io/appforpub/) enables real-time risk calculation through intuitive input of clinical indicators, providing risk stratification and personalized clinical recommendations.

The RF model achieves high-accuracy and interpretable hernia risk prediction in PD patients. The R Shiny-based online tool facilitates clinical risk stratification and early intervention, improving patient prognosis.