Machine-learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis

Abstract

Abstract Background An appropriate prediction model for the adverse prognosis before peritoneal dialysis (PD) is lacking. Therefore, we retrospectively analyzed patients who received PD to construct a predictive model for adverse prognoses using machine learning (ML). Methods A retrospective analysis was conducted on 873 patients who underwent PD from August 2007 to December 2020. Five commonly used machine learning algorithms are used for initial model training. Using the area under the curve and accuracy, we ranked the indicators with the highest impact and displayed them using the Shapley additive explanation (version 0.41.0) values, from which the top 20 indicators were selected to build a compact model conducive to clinical application. All model building steps are implemented in Python (version 3.8.3). Results A total of 824 patients were included in the analysis at the end of follow-up, 353 patients withdrew from PD (converted to haemodialysis or died), and 471 patients continued receiving PD. In complete model, the CatBoost model exhibited the strongest performance (AUC: 0.80, 95% CI: 0.76–0.83; ACC: 0.78, 95%CI: 0.72–0.83) and was selected for subsequent analysis. We reconstructed a compression model by extracting 20 key features ranked by the SHAP values, the Catboost model also showed the strongest performance (AUC: 0.79; ACC: 0.74). Conclusions The Catboost model built using the intelligent analysis technology of ML demonstrated the best predictive performance. Thus, our developed prediction model has potential value in patient screening before PD and hierarchical management after peritoneal dialysis.