Interpretable machine learning models for predicting cognitive impairment using NHANES neuropsychological tests: nutritional and sociodemographic associations.

ABSTRACT:

Background

Early identification of individuals at risk for cognitive impairment is essential for timely intervention and public health planning. While sociodemographic and clinical predictors are well recognized, the role of nutrition and its interactions in cognitive health remains less explored.

Methods

Using data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES, n = 2,208), we developed ensemble machine learning models (LightGBM, XGBoost, Random Forest) to predict cognitive impairment across three neuropsychological assessments (CERAD-WL, DSST, AFT). SHapley Additive exPlanations (SHAP) were applied to quantify and interpret the contribution of demographic, clinical, and nutritional predictors, as well as their interactions. To validate t

SUBMITTER: Song L 

PROVIDER: S-EPMC12847056 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature