Efficacy of six machine learning models in predicting the risk of peritoneal dialysis-related peritonitis
YANG Fang
ZHAO Jianqiu
QIE Shuwen
YANG Li
Abstract：Objective:To construct risk prediction models for peritoneal dialysis-associated peritonitis(PDAP)based on different machine learning(ML)algorithms,providing a reference for identifying high-risk patients.Methods:Retrospectively collect patients who underwent peritoneal dialysis in Guizhou Provincial People's Hospital from December 2009 to May 2024.Randomly divide them into a training set and a validation set at a ratio of 7∶3.In the training set,independent variables were screened through Lasso regression.Risk prediction models for PDAP were constructed based on six machine-learning algorithms,namely Logistic regression(LR),decision tree,support vector machines,random forest(RF),extreme gradient boosting,and artificial neural network.The performance of the models was evaluated based on the area under the receiver operating characteristic curve(AUC),accuracy,precision,recall,and F1-score,and the optimal model was selected.Results:A total of 982 peritoneal dialysis patients were included,among whom 221 patients developed PDAP,with an incidence rate of 22.51%.After five independent variables were screened out by LASSO regression based on ten-fold cross-validation,six ML models were constructed.In the training set,LR(AUC=0.800)performed the best compared with other models.In the validation set,RF(AUC=0.772)had the best performance.The LR model had a relatively high AUC value in the training set,which might indicate over-fitting.Further,based on the RF model,the feature variables were ranked in terms of importance,in the order of dialysis vintage,white blood cell count,contact-line contamination,catheter exit-site infection and/or tunnel infection,and constipation or diarrhea.Conclusion:The PDAP risk prediction model constructed based on the RF algorithm has the optimal performance,which can assist clinical medical staff in early assessment and prevention of PDAP.
Keywords：machine learningperitoneal dialysis-associated peritonitisrisk prediction model
Publication Date：2025-12-25
Online Publishing Date：2026-01-09(First online date of this platform, not the publication date of the document)
Pages：6( 4626-4631 )