Interpretable Machine Learning for Cognitive Impairment Assessment: SHAP-Based Analysis of XGBoost Models Using NHANES 2011-2014 Cognitive Tests in Older Adults

Background: Cognitive impairment in older adults is a key risk factor for dementia, yet early detection remains challenging. Traditional models lack interpretability and struggle to capture complex risk patterns. Interpretable machine learning offers a solution by combining predictive accuracy with transparency. This study uses National Health and Nutrition Examination Survey (NHANES) 2011–2014 data to develop explainable models for domain-specific cognitive tests in adults aged 60 and above. Objective: This study establishes a novel explainable AI framework that bridges interpretable machine learning models for domain-specific cognitive impairment assessment in adults over 60 years old, leveraging the NHANES 2011-2014 dataset to identify critical biomarkers and modifiable risk factors. Methods: Four cognitive tests—Digit Symbol Substitution Test (DSST), Delayed Recall Test (DRT), Animal Fluency Test (AFT), and Immediate Recall Test (IRT)—were individually modeled using XGBoost regression. Model robustness was ensured through five-fold cross-validation and grid search hyperparameter optimization. SHapley Additive exPlanations (SHAP) were applied to elucidate feature contributions. Results: The optimized models achieved superior predictive performance: DSST (RMSE = 12.473, R² = 0.459, MAE = 9.975), DRT (RMSE = 2.004, R² = 0.148, MAE = 1.617), AFT (RMSE = 5.043, R² = 0.191, MAE = 3.977), and IRT (RMSE = 4.082, R² = 0.197, MAE = 3.252). Conclusion: This is the first study to establish individualized XGBoost models for distinct NHANES cognitive domains, demonstrating how SHAP-driven interpretability enhances early screening strategies. Our findings highlight metabolic markers (e.g., Diabetes Mellitus) and lifestyle factors as actionable targets for dementia prevention in aging populations.