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

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.

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Publication Details

Journal
Frontiers in Nutrition
Published
2026-01-14
DOI
https://doi.org/10.3389/fnut.2025.1680290
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Xiaojiao Xiang, L. Song, Chenlu Li, Peijia Lin
Frontiers in Nutrition
Dementia and Cognitive Impairment Research
article

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

Xiaojiao Xiang, L. Song, Chenlu Li, Peijia Lin
article en

Abstract

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.

Frontiers in NutritionVol. 12
Dalian Medical University (CN), Second Affiliated Hospital of Chongqing Medical University (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Children's Hospital of Chongqing Medical University (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 89%
Dementia and Cognitive Impairment Research
DOIOpen Access PDFOpenAlex
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