Related Experiment Video
Updated: Jan 31, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Interpretable machine learning models for predicting cognitive impairment using NHANES neuropsychological tests:
Li Song1, Chenlu Li2, Xiaojiao Xiang3
1Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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 the nutrient interactions identified by our models, we conducted exploratory in vitro experiments assessing oxidative stress and neuroprotective pathways in SH-SY5Y neuronal cells.
Results:
Ensemble models demonstrated excellent predictive performance, consistently outperforming traditional classifiers. Key predictors included education, age, socioeconomic status, and chronic disease conditions. Among nutritional factors, vitamin B2 emerged as consistently associated with lower predicted cognitive impairment risk across all three models, with notable interactions observed with copper and vitamin E. Exploratory in vitro experiments supported these associations, showing reduced oxidative stress and increased expression of neuroprotective genes (SIRT1, BDNF) under vitamin B2 treatment, particularly when combined with copper or vitamin E.
Conclusion:
Interpretable machine learning models integrating cognitive tests with demographic, clinical, and nutritional variables can accurately predict cognitive impairment. Nutritional predictors, particularly vitamin B2 and its interactions, may contribute to model performance and biological plausibility, suggesting potential avenues for stratified monitoring strategies.
More Related Videos
08:35Shuttle Box Assay as an Associative Learning Tool for Cognitive Assessment in Learning and Memory Studies using Adult Zebrafish
Published on: July 12, 2021
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
Related Concept Videos
01:27Associative Learning
Classical conditioning, also known...
01:21Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
02:11The Roles of Bacteria and Fungi in Plant Nutrition
01:38Cognitive Dissonance
01:30Simplified Synchronous Machine Model
In this model, each generator is connected to a...
01:24Wind Turbine Machine Models
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...