Identifying influential environmental drivers of Alzheimer's disease at the county level using machine learning and explainable artificial intelligence

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Abstract
Background: Alzheimer's disease (AD) prevalence varies substantially across United States (US) counties, yet the combined influence of environmental and socioeconomic factors remains incompletely characterized. Objective: This study bridges this gap by employing machine learning combined with explainable artificial intelligence to identify and interpret key environmental drivers of AD prevalence across US counties. Methods: Utilizing county-level AD prevalence data linked with comprehensive environmental and socioeconomic indicators, we applied an eXtreme Gradient Boosting (XGBoost) regression framework. SHapley Additive exPlanations (SHAP) were used to evaluate global and local feature contributions. Results: The final model demonstrated stable performance (RMSE: 0.89 ± 0.04; R 2 : 0.62 ± 0.03). Among 15 selected factors, chloride precipitation and median household value emerged as the most influential predictors. Additional significant drivers included land-use metrics (open land, walkability), atmospheric deposition, and localized industrial exposures. SHAP analysis revealed nonlinear, threshold-like increases in predicted AD prevalence at higher chloride levels, while geographic mapping identified regionally distinct exposure profiles. Conclusions: Geospatial analysis and explainable AI demonstrate that AD prevalence is driven by interacting environmental toxicants and socioeconomic disadvantages. This “exposome” perspective highlights that mitigating environmental hazards and systemic inequities is critical for reducing the neurodegenerative disease burden.

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