Using Machine Learning to Predict Cognitive Performance from Lifestyle Factors

A recent study has demonstrated that machine learning algorithms can effectively predict cognitive performance based on various lifestyle and health indicators. Researchers focused on identifying which factors, such as diet, physical activity, and body measurements, are most closely associated with brain function across different stages of life. The study involved analyzing data from 374 adults aged 19 to 82, including demographics like age, BMI, blood pressure, food intake patterns, and physical activity levels. Participants completed a cognitive test called the flanker task, which assesses attention and inhibitory control by requiring quick responses without distraction.

The team discovered that age, blood pressure, and BMI were the top predictors of performance, especially on the flanker task. While diet quality also influenced results, its predictive power was less strong compared to blood pressure and body weight, but still correlated with better cognitive performance. The study highlighted that lifestyle factors such as diet, physical activity, and body measurements interact to influence cognitive health.

Using various machine learning methods, the researchers assessed which variables contributed most to predicting task accuracy and reaction time. They found that age had the greatest impact, followed by diastolic blood pressure, BMI, and systolic blood pressure. Physical activity was identified as a moderate predictor, indicating its role in cognitive performance, especially when combined with diet and body weight. These findings suggest that lifestyle modifications, including blood pressure management, maintaining a healthy weight, and engaging in physical activity, can support cognitive functions.

The study underscores the potential of machine learning to analyze complex data and uncover nuanced relationships between health behaviors and brain performance. It opens pathways for personalized strategies to enhance cognitive aging and manage risk factors for cognitive decline, especially in populations with metabolic concerns. As highlighted by lead researcher Naiman Khan, this approach moves beyond traditional statistical methods, allowing for more tailored interventions that could benefit individuals seeking to optimize mental health through lifestyle changes.

Published in The Journal of Nutrition, the research emphasizes the importance of a holistic view of lifestyle choices in maintaining cognitive health throughout life. It also aligns with existing evidence linking diet patterns such as the DASH, Mediterranean, and MIND diets to cognitive protection, alongside physical health factors like blood pressure and BMI.

In conclusion, this innovative application of machine learning illustrates how integrated health behaviors impact cognitive performance, providing valuable insights for future research and health strategies aimed at promoting brain health in aging populations.