Applying machine learning to associate clinical factors with malnutrition risk in peritoneal dialysis patients: an internally validated interpretable model

Objective Malnutrition frequently complicates peritoneal dialysis (PD) and associates with adverse outcomes, underscoring the clinical importance of its timely identification. This study aimed to develop and internally validate a machine learning-based assessment model to identify PD patients currently at malnutrition risk who need nutritional intervention. Methods In this cross-sectional study, 144 PD patients were evaluated for malnutrition risk using the Patient-Generated Subjective Global As