Machine learning to predict dementia for American Indian and Alaska native peoples: a retrospective cohort study.

Background: Dementia is an increasing concern among American Indian and Alaska Native (AI/AN) communities, yet machine learning models utilizing electronic health record (EHR) data have not been developed or validated for this population. This study aimed to develop a two-year dementia risk prediction model for AI/AN individuals actively using Indian Health Service (IHS) and Tribal health services.

Methods: Seven years of data were obtained from the IHS National Data Warehouse and related EHR databases and divided into a five-year baseline period (FY2007-2011) and a two-year dementia prediction period (FY2012-2013). Four algorithms were assessed: logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Dementia Risk Score (DRS)-based and extended models were developed for each algorithm, with performance evaluated by the area under the receiver operating characteristic curve (AUC).

Findings: The study cohort included 17,398 AI/AN adults aged ≥ 65 years who were dementia-free at baseline, of whom 59.8% were female. Over the two-year follow-up, 611 individuals (3.5%) were diagnosed with incident dementia. Extended models for logistic regression, LASSO, and XGBoost performed comparably: AUCs (95% CI) of 0.83 (0.79, 0.86), 0.83 (0.79, 0.86), and 0.82 (0.79, 0.86). These top-performing models shared 12 of the 15 highest-ranked predictors, with novel predictors including service utilization.

Interpretation: Machine learning algorithms utilizing EHR data can effectively predict two-year dementia risk among AI/AN older adults. These models could aid IHS and Tribal health clinicians in identifying high-risk individuals, facilitating timely interventions and improved care coordination.

Funding: NIH.