Optimizing Nursing Communication for Symptom Management in Hemodialysis: Development of an Artificial Intelligence–Based Web Predictive Model for Burden Classification and Evidence Navigation

This study demonstrates the potential of machine learning models to improve symptom burden classification in HD patients. The XGBoost model, utilizing a limited set of key predictors, offers high predictive accuracy and clinical utility, providing a scalable solution for resource-constrained healthcare settings. This approach supports personalized symptom management, contributing to improved patient outcomes and efficient resource use in nephrology nursing.