ORIGINAL RESEARCH article
Front. Med.
Sec. Nephrology
Machine Learning-Based Prediction of Hernia Risk in Peritoneal Dialysis Patients: A Comparative Study of Models and SHAP-Driven Interpretability Analysis
Yugang Cao
xun Hu
Tao Fang
Jun Guo
Huangshi Central Hospital, Huangshi, China
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Abstract
Purpose:This study aimed to predict the hernia risk in peritoneal dialysis patients using machine learning(ML) models and conduct an interpretability analysis. Methods: A total of 1144 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. Results: 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. Conclusion: 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.
Summary
Keywords
end-stage renal disease, Hernia Risk Prediction, machine learning, Peritoneal Dialysis, SHAP analysis
Received
16 August 2025
Accepted
13 February 2026
Copyright
© 2026 Cao, Hu, Fang and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jun Guo
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