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

ABSTRACT:

Background

Machine learning (ML) use in health research is growing, yet its application to predict cognitive outcomes using diverse health indicators is underinvestigated.

Objectives

We used ML models to predict cognitive performance based on a set of health and behavioral factors, aiming to identify key contributors to cognitive function for insights into potential personalized interventions.

Methods

Data from 374 adults aged 19-82 y (227 females) were used to develop ML models predicting cognitive performance (reaction time in milliseconds) on a modified Eriksen flanker task. Features included demographics, anthropometric measures, dietary indices (Healthy Eating Index, Dietary Approaches to Stop Hypertension, Mediterranean, and Mediterranean-Dietary Approaches to S

SUBMITTER: Verma S 

PROVIDER: S-EPMC12308081 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature