[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126197-en":3,"doc-seo-126197-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126197,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction","This study examines the association between vitamin D and cutaneous melanoma and develops an explainable machine learning prediction model. Data were drawn from the NHANES program across three cycles (2011–2012, 2013–2014, 2015–2016), incorporating self-reported melanoma status, serum vitamin D levels, and covariates. Logistic regression with NHANES weights assessed risks, while forest plots evaluated additional factors. Ten machine learning models were compared, with XGBoost selected.","TYPE Original Research PUBLISHED 08 May 2025  \nDOI 10.3389/fonc.2025.1503611  \nOPEN ACCESS  \nEDITED BY  \nChiara Moltrasio,  \nFondazione IRRCS Ca’ Granda Ospedale Maggiore Policlinico, Italy  \nREVIEWED BY  \nGoutam Saha,  \nUniversity of Dhaka, Bangladesh Maurizio Romagnuolo,  \nIRCCS Ca ‘Granda Foundation Maggiore Policlinico Hospital, Italy  \n*CORRESPONDENCE  \nLingyi Li  \n [winglilingyi@163.com](winglilingyi@163.com)[ ](winglilingyi@163.com)Man Luo  \n [sunshineman2023@163.com](sunshineman2023@163.com)  \nRECEIVED 04 November 2024  \nACCEPTED 14 April 2025  \nPUBLISHED 08 May 2025  \nCITATION  \nLi L and Luo M (2025) Exploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction.  \nFront. Oncol. 15:1503611 .  \ndoi: 10.3389/fonc.2025.1503611  \nCOPYRIGHT  \n© 2025 Li and Luo. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nExploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction  \nLingyi Li 1* and Man Luo 2*  \n1Outpatient Department Office, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China, 2 Department of Oncology, Wuhan No.1 Hospital, Wuhan, Hubei, China  \nObjective: This study aims to examine association between vitamin D with melanoma and develop an explainable machine learning model.  \nMethods: For this study, relevant data were downloaded from the CDC ’s National Health and Nutrition Examination Survey (NHANES) program, for the three survey cycles 2011-2012, 2013–2014 and 2015-2016 . Self-reported melanoma data, serum vitamin D levels, and other covariates were downloaded and analyzed. Analysis of variance in this study was performed using t-tests and chi-square tests, modelling was performed using logistic regression based on NHANES weights, and other risk factors were analyzed using forest plots. Ten machine learning models were compared and XGboost was selected for the melanoma prediction.  \nResults: In this study, logistic regression analysis revealed a protective effect of higher vitamin D levels in melanoma, the ORs were much less than 1 for Q2 (OR=0.97, 95% CI (0.44, 0.98)), Q3 (OR=0.71, 95% CI (0.65, 0.92)), and Q4 (OR=0 .32, 95% CI (0 . 55, 0 . 81)) . Meanwhile, forest plot analysis showed that vitamin D, the number of sunburns in the past year, advanced age, Caucasian, education some college, single and unmarried, smoking, diabetes and hypertension, were all statistically significant. The OR was higher in men than in women, with Q4 values of 0.31 (95% CI: 0.18–0.51) for men and 0.29 (95% CI: 0.15–0.45) for women. OR was higher in the senior patients than in the nonsenior group, with Q4 (OR=0 . 53, 95% CI (0 . 23, 0 .73)) . An explainable XGBoost model had AUC 0.906, and in the model vitamin D had main contribution to the model.  \nConclusion: In conclusion, this study concluded that vitamin D decreases melanoma risk based on a larger sample and multi-covariate analysis. Female and young people received high protection from vitamin D in melanoma. XGBoost can accurately prediction the possibility of melanoma based on vitamin D.  \nKEYWORDS  \nvitamin D, cutaneous melanoma, male, aging, NHANES  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nOver the past 50 years, cutaneous melanoma (CM) incidence has steadily increased around the world. The expected number of new cases ofCM in the world in 2018 is 287,723, with an age-standardized incidence rate of 3.1/100,000/year and a mortality rate of 0.63/100,000/ year, according to GLOB","cbCaiewtDGAgK49l","https://ap.wps.com/l/cbCaiewtDGAgK49l","pdf",3130617,1,12,"English","en",105,"# Objective\n# Methods\n## Data source and variables\n## Statistical and machine learning analyses\n# Results\n## Logistic regression findings\n## Forest plot risk factors\n## Explainable XGBoost performance","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"To assess the association between vitamin D and cutaneous melanoma and to develop an explainable machine learning model for melanoma prediction.\"},{\"question\":\"Which data source and time periods were used?\",\"answer\":\"Relevant variables were downloaded from NHANES for three survey cycles: 2011–2012, 2013–2014, and 2015–2016.\"},{\"question\":\"What machine learning model was selected and how well did it perform?\",\"answer\":\"Ten models were compared, and XGBoost was selected; the explainable model achieved an AUC of 0.906 with vitamin D as a main contributor.\"}]","Exploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction | PDF",1785903743,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"exploration-in-association-between-vitamin-d-and-cutaneous-melanoma-and-explainable-machine-learning-prediction","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploration-in-association-between-vitamin-d-and-cutaneous-melanoma-and-explainable-machine-learning-prediction/126197/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study’s main objective?","Question",{"text":76,"@type":77},"To assess the association between vitamin D and cutaneous melanoma and to develop an explainable machine learning model for melanoma prediction.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data source and time periods were used?",{"text":81,"@type":77},"Relevant variables were downloaded from NHANES for three survey cycles: 2011–2012, 2013–2014, and 2015–2016.",{"name":83,"@type":74,"acceptedAnswer":84},"What machine learning model was selected and how well did it perform?",{"text":85,"@type":77},"Ten models were compared, and XGBoost was selected; 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