{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T10:43:28Z","timestamp":1793357008673,"version":"4.5.0"},"reference-count":64,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Food Science &amp; Nutrition"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The growing burden of non\u2010melanoma skin cancer (NMSC) among older adults underscores the need for early risk prediction based on modifiable dietary factors. In this cross\u2010sectional study using data from the 2007\u20132010 and 2017\u20132018\u2009cycles of the National Health and Nutrition Examination Survey (NHANES), we evaluated dietary antioxidant profiles and developed an interpretable machine learning model for NMSC risk prediction. Baseline feature analysis revealed that participants with NMSC generally had higher socioeconomic status and higher oxidative balance scores (OBS), with significant differences also observed in age and race. Through feature importance ranking using the Random Forest (RF) algorithm, caffeine and luteolin were identified as the most predictive dietary variables. After comparing 12 machine learning algorithms, linear discriminant analysis (LDA) was selected as the final screening\u2010oriented prediction model (AUC\u2009=\u20090.742). Although its overall accuracy was lower than that of several ensemble models, LDA demonstrated higher sensitivity (0.730) and favorable imbalance\u2010aware performance, thereby mitigating the \u201caccuracy paradox\u201d and supporting its use for preliminary screening to identify participants with higher predicted NMSC risk. SHapley Additive exPlanations (SHAP) global explanations further confirmed that, after controlling for age as a primary risk factor, niacin and quercetin act as protective factors in the NMSC risk prediction model. In summary, this study developed an interpretable and robust machine learning model for NMSC risk prediction in older adults and provides hypothesis\u2010generating evidence regarding the potential role of dietary antioxidant profiles in prevention\u2010oriented strategies.<\/jats:p>","DOI":"10.1002\/fsn3.72458","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T07:05:14Z","timestamp":1792479914000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Nutritional Epidemiology to Clinical Prediction: Interpretable Machine Learning Reveals Subtle Associations Between Dietary Antioxidant Profiles and Non\u2010Melanoma Skin Cancer in Older Adults"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4495-6003","authenticated-orcid":false,"given":"Bo Han","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Health Industry Sookmyung Women's University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6619-0260","authenticated-orcid":false,"given":"Ji Young","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Health Industry Sookmyung Women's University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2201-1280","authenticated-orcid":false,"given":"Kyung Eun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Health Industry Sookmyung Women's University  Seoul Republic of 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