Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data
This work addresses cognitive health disparities in Hispanic populations, who face disproportionate risk for Alzheimer's and related dementias, by leveraging social determinants of health for early prediction.
The study tackled early prediction of Alzheimer's and related dementias by using social determinants of health data, employing ensemble regression trees to predict 4-year and 9-year cognitive scores and decline in Hispanic populations, identifying key predictive factors to inform interventions.
Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors play a crucial role, emerging research reveals that social determinants of health (SDOH) significantly influence both the risk and progression of cognitive functioning, such as cognitive scores and cognitive decline. This report examines how these social, environmental, and structural factors impact cognitive health trajectories, with a particular focus on Hispanic populations, who face disproportionate risk for AD/ADRD. Using data from the Mexican Health and Aging Study (MHAS) and its cognitive assessment sub study (Mex-Cog), we employed ensemble of regression trees models to predict 4-year and 9-year cognitive scores and cognitive decline based on SDOH. This approach identified key predictive SDOH factors to inform potential multilevel interventions to address cognitive health disparities in this population.