BackgroundSurvival after an Alzheimer's disease (AD) diagnosis is vital for patients, their families, caregivers, and healthcare providers. Hawaii, known for its diverse ethnic population, exhibits significant racial health disparities.ObjectiveThis study examined racial/ethnic and socioeconomic disparities in AD survival in Hawaii and developed machine learning models to predict overall survival using Hawaii Medicare data.MethodsNine years of Hawaii Medicare data were utilized to gather information on AD development after age 65, following patients to capture all-cause survival or until censoring. The study examined the effects of race/ethnicity and socioeconomic status (SES) on mortality risk. Cox regression analysis was conducted on overall survival, accounting for covariates. A Survival Random Forest was employed to model survival, incorporating