Interpretable machine learning models predict cognitive impairment in adults aged 50 and over with diabetes: influence of dietary nutrients.

Introduction: diabetes mellitus increases the risk of cognitive impairment, but the role of dietary nutrients remains unclear. Objectives: to develop interpretable machine learning (ML) models to identify associations with cognitive impairment in adults aged 50 years and older with diabetes, and to identify key dietary nutrients associated with cognitive outcomes. Methods: data from the 2011-2014 National Health and Nutrition Examination Survey were analyzed. Cognitive function was assessed using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word Learning Test, the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). A total of 46 dietary nutrients and other covariates were included. Feature selection was performed using the Boruta algorithm. Six ML models were trained with ten-fold cross-validation. SHapley Additive Explanations and Local Interpretable Model-Agnostic Explanations were applied for model interpretation. Results: XGBoost achieved the highest performance in the CERAD model (AUC = 0.982), whereas Random Forest outperformed other models in the AFT and DSST models (AUC = 0.958 and 0.856, respectively). Caffeine emerged as a key protective factor. Copper, zinc, and moisture intake were also associated with reduced risk of cognitive impairment. Conclusions: interpretable ML models can effectively predict cognitive impairment in older adults with diabetes. Nutritional profiling may support early screening and targeted intervention strategies based on observed associations.