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

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

objectivesWe 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.

methodsData 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 Stop Hypertension Intervention for Neurodegenerative Delay), self-reported physical activity, and systolic and diastolic blood pressures. The data set was split (80:20) for training and testing. Predictive models (decision trees, random forest, AdaBoost, XGBoost, gradient boosting, linear, ridge, and lasso regression) were used with hyperparameter tuning and crossvalidation. Feature importance was calculated using permutation importance, whereas performance using mean absolute error (MAE) and mean squared error.

resultsRandom forest regressor exhibited the best performance, with the lowest MAE (training: 0.66 ms; testing: 0.78 ms) and mean squared error (training: 0.70 ms

conclusionsAge, blood pressure, and BMI show strong associations with cognitive performance, whereas diet quality has a subtler effect. These findings highlight the potential of ML models for developing personalized interventions and preventive strategies for cognitive decline.