Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease

Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (ML) models to predict the malnutrition status of children with CHD. We used explainable ML methods to provide insight into the model's predictions and outcomes.This prospective cohort study included consecutive...