Exploring predictors of low nutritional literacy in maintenance hemodialysis patients: a machine learning and network analysis approach

作者：Yao L, Liu Y, Tan X, Chen S, Fang Y · 发表于：Frontiers in public health · 年份：2026 · DOI：10.3389/fpubh.2026.1862524 · 被引用次数：37 · 研究领域：Renal Dialysis、Machine Learning、Health Literacy、Nutritional Status、Humans、Female、Male、Middle Aged、Self Efficacy、Social Support、Aged、Self Care

BACKGROUND: Patients undergoing maintenance hemodialysis (MHD) commonly experience substantial self-care challenges due to the complexity and highly restrictive nature of their treatment regimens. Nutritional literacy is a critical determinant of effective self-management in this population. However, the main predictors of low nutritional literacy and their relationships remain unclear, limiting early targeted interventions. This study aimed to identify important predictors of low nutritional literacy in MHD patients and to explore how these predictors relate to each other. METHODS: This study included 712 patients on maintenance hemodialysis (MHD). Latent profile analysis was used to identify individuals with low nutritional literacy, followed by machine learning to screen for key predictors. SHAP analysis was employed to visualize the importance of these predictors. Finally, network analysis was used to investigate the underlying relationships among the key predictors. RESULTS: The LightGBM model demonstrated the best predictive performance, with an AUC of 0.817 and an F1-score of 0.738. The LightGBM model identified nine key predictors: social support, depression, anxiety, self-efficacy, educational level, dialysis frequency, age, living arrangement (living with a spouse and children), and place of residence. Among these, social support had the highest SHAP value. Network analysis revealed that self-efficacy served as the central predictor(rs = 0.56), showing a significa...