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Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning.

Shreya Verma1, Tori A Holthaus2, Shelby Martell3

  • 1Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, United States.

The Journal of Nutrition
|May 14, 2025
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Summary

Machine learning models predict cognitive performance using health data. Age, blood pressure, and BMI are key factors, suggesting personalized interventions for cognitive health.

Keywords:
MIND dietcognitive functiondietary patternspersonalized healthrandom forest

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Area of Science:

  • Health Informatics
  • Cognitive Neuroscience
  • Machine Learning

Background:

  • Machine learning (ML) applications in health research are expanding.
  • Predicting cognitive outcomes with health indicators using ML is understudied.

Purpose of the Study:

  • Utilize ML models to predict cognitive performance.
  • Identify key health and behavioral contributors to cognitive function.
  • Inform personalized interventions for cognitive health.

Main Methods:

  • Developed ML models using data from 374 adults (aged 19-82).
  • Included demographics, anthropometrics, dietary indices, physical activity, and blood pressure as features.
  • Employed various regression models with hyperparameter tuning and cross-validation.

Main Results:

  • Random forest regressor achieved the best performance.
  • Age, diastolic blood pressure, BMI, and systolic blood pressure were significant predictors.
  • Healthy Eating Index showed a subtler effect, while ethnicity and sex had minimal impact.

Conclusions:

  • Age, blood pressure, and BMI strongly correlate with cognitive performance.
  • Diet quality has a less pronounced but notable association.
  • ML models show promise for personalized cognitive health interventions and prevention strategies.