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Four ML models were tested for each setting; SHapley Additive exPlanations (SHAP) was applied to identify key classification features of the best model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Univariate analyses targeting 399 patients with CRS demonstrated significant differences for 17 and eight additional features in both settings and Setting\u20102, respectively. Setting\u20102 revealed an increased incidence of eosinophilic CRS without nasal polyps (NPs) with increasing severity. Random forest and eXtreme Gradient Boosting were the best ML models in Setting\u20101 and \u20102, respectively. Based on SHAP, the blood basophil count was one of the three most important classification features for any class. In Setting\u20102, high C\u2010reactive protein levels, high blood basophil count, comorbid chronic eosinophilic pneumonia, low computed tomography (CT) scores in the maxillary sinus, and low NP scores were the five most important classification features separating an AEC \u2265\u00a01500\/\u00b5L from other classes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML classification of CRS revealed the involvement of basophils in eosinophilia in peripheral blood. Features specific for CRS with an AEC \u2265\u00a01500\/\u00b5L indicated that otolaryngologists should suspect the potential eosinophilic CRS with unique comorbidities even in the absence of NPs and the unremarkable inflammation on sinonasal CT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alr.70077","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T07:09:08Z","timestamp":1764918548000},"page":"353-368","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clinical Key Features Uncovered by Blood Eosinophilia\u2010Based Machine Learning Classification of Chronic Rhinosinusitis"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3638-5366","authenticated-orcid":false,"given":"Masaaki","family":"Ishikawa","sequence":"first","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery Hyogo Prefectural Amagasaki General 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