ArticleJournal of Alzheimer's disease reports
Identifying influential environmental drivers of Alzheimer's disease at the county level using machine learning and explainable artificial intelligence.
Article in Journal of Alzheimer's disease reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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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 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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