Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches

Logistic regression outperformed more complex machine learning models, providing the best combination of predictive accuracy and interpretability for identifying MCI. Across models, age, ethnicity, functional difficulties, social disconnectedness, and stress consistently emerged as key predictors, highlighting their central role in cognitive health. These findings suggest that psychosocial and functional measures can serve as practical indicators for those who need to be screened early for MCI, offering an opportunity for timely intervention and support. However, future work should include longitudinal data and clinical diagnoses to validate and refine these predictive tools.