Using machine learning to identify risk factors for Alzheimer's disease among older adults in the United States: The role of chronic and behavioral health.

BACKGROUND

The interactions between behavioral disturbances, chronic diseases, and Alzheimer's disease (AD) risk are not fully understood, particularly in the context of the COVID-19 pandemic.

OBJECTIVE

This study aimed to identify key demographic, behavioral, and health-related predictors of AD using machine learning approaches.

METHODS

We conducted a cross-sectional analysis of 3257 participants from the National Health and Aging Trends Study (NHATS) and its COVID-19 supplement. Predictors included demographic, behavioral, and chronic disease variables, with self-reported physician-diagnosed AD as the outcome. LASSO and random forest (RF) models identified significant predictors, and regression tree analysis examined interactions to estimate individual AD risk profiles and subgroups.

RESULTS

Stroke, diabetes, osteoporosis, depression, and sleep disturbances emerged as key predictors of AD in both LASSO and RF models. Regression tree analysis identified three risk subgroups: a high-risk subgroup with a history of stroke and diabetes, showing a 68% AD risk among females; an intermediate-risk subgroup without stroke but with osteoporosis and positive COVID-19 status, showing a 30% risk; and a low-risk subgroup without stroke or osteoporosis, with the lowest risk (∼10%). Female patients with both stroke and diabetes had significantly higher AD risk than males (68% versus 10%, p = 0.029). Among patients without stroke but with osteoporosis, COVID-19 positivity increased AD risk by 20% (30% versus 10%, p = 0.006).

CONCLUSIONS

Machine learning effectively delineates complex AD risk profiles, highlighting the roles of vascular and metabolic comorbidities and the modifying effects of sex, osteoporosis, and COVID-19. These insights support targeted screening and early intervention strategies to improve outcomes in older adults.