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Related Experiment Video

Updated: Jun 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A

Yuwen ShangGuan1,2, Kunpeng Wu2, Dong Li3

  • 1Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.

CNS Neuroscience & Therapeutics
|May 30, 2026
PubMed
Summary

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Dietary antioxidants like vitamin E and flavonoids can predict depression risk in older adults. Machine learning identified key nutrients and their interactions for personalized risk assessment and nutritional interventions.

Area of Science:

  • Gerontology and Nutritional Psychiatry
  • Computational Medicine and Bioinformatics

Background:

  • Physically inactive older adults are a vulnerable group for depression.
  • The role of dietary antioxidant intake in stratifying depression risk in this population remains unclear.
  • This study investigates the predictive capacity of dietary antioxidants for depression risk in older adults.

Purpose of the Study:

  • To evaluate the predictive ability of dietary antioxidant intake for depression risk in physically inactive adults aged 60 and older.
  • To utilize machine learning methods for identifying key dietary predictors and their interactions.
  • To develop a tool for personalized depression risk assessment based on dietary intake.

Main Methods:

  • Analysis of 2,496 physically inactive adults aged 60+ from NHANES 2007-2010 and 2017-2018 cycles.
Keywords:
SHAPdepressiondietary antioxidantselderlymachine learningphysical inactivity

Related Experiment Videos

Last Updated: Jun 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

  • Assessment of 44 dietary antioxidants, Composite Dietary Antioxidant Index (CDAI), and Oxidative Balance Score (OBS).
  • Application of random forest for feature selection, followed by six machine learning models (Random Forest, XGBoost, KNN, SVM, Decision Tree, Naïve Bayes) with cross-validation and SHAP analysis for risk interpretation.

    • Main Results:

    • The random forest model achieved high performance (94.9% accuracy, 0.943 ROC AUC).
    • Key predictors included vitamin E, luteolin, total flavonoids, copper, magnesium, and iron, showing inverse associations with depression risk.
    • Synergistic protective effects were observed between vitamin E/copper and luteolin/flavonoids; an online risk prediction tool was developed.

    Conclusions:

    • Dietary antioxidant intake is a significant predictor of depression risk in physically inactive older adults.
    • Machine learning and SHAP analysis identified crucial nutrients and interactions for targeted nutritional interventions.
    • The findings support the use of dietary data for early depression screening and personalized management strategies in older adults.