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.

Abstract

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.