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SHapley Additive exPlanations (SHAP) were used to evaluate global and local feature contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final model demonstrated stable performance (RMSE: 0.89\u2009\u00b1\u20090.04; R\n                      <jats:sup>2<\/jats:sup>\n                      : 0.62\u2009\u00b1\u20090.03). 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Geospatial analysis and explainable AI demonstrate that AD prevalence is driven by interacting environmental toxicants and socioeconomic disadvantages. This \u201cexposome\u201d perspective highlights that mitigating environmental hazards and systemic inequities is critical for reducing the neurodegenerative disease burden.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/25424823261449503","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:05:21Z","timestamp":1779278721000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying influential environmental drivers of Alzheimer's disease at the county level using machine learning and explainable artificial intelligence"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4062-2267","authenticated-orcid":false,"given":"Ria","family":"Martins","sequence":"first","affiliation":[{"name":"College of Public Health, The Ohio State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3719-7356","authenticated-orcid":false,"given":"Yanni","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Public Health, The Ohio State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5864-3186","authenticated-orcid":false,"given":"Jianyong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Public Health, The Ohio State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.v67n1118"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.37022\/jiaps.v7i1.274"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40120-022-00338-8"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiochem.2019.04.015"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(02)11920-9"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/alz.12362"},{"key":"e_1_3_3_8_2","unstructured":"Alzheimer\u2019s Association. 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