{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T16:01:38Z","timestamp":1785168098851,"version":"3.55.0"},"reference-count":21,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010272","name":"Zarqa University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Genetic Engineering and Biotechnology"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jgeb.2026.100695","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T00:01:56Z","timestamp":1777075316000},"page":"100695","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Healthcare Predictions: unmasking the strengths and weaknesses of AI models for vitamin D deficiency assessment"],"prefix":"10.1016","volume":"24","author":[{"given":"Ahmad","family":"Al-Qerem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruzayn","family":"Quddoura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulhakeem","family":"Issa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2334-4709","authenticated-orcid":false,"given":"Asma","family":"Sbaih","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.jgeb.2026.100695_b0005","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s11154-017-9424-1","article-title":"The vitamin D deficiency pandemic: approaches for diagnosis, treatment and prevention","volume":"18","author":"Holick","year":"2017","journal-title":"Reviews in Endocrine and Metabolic Disorders"},{"issue":"3","key":"10.1016\/j.jgeb.2026.100695_b0010","doi-asserted-by":"crossref","first-page":"593","DOI":"10.3390\/nu15030593","article-title":"The one-hundred-year anniversary of the discovery of the sunshine vitamin D3: historical, personal experience and evidence-based perspectives","volume":"15","author":"Holick","year":"2023","journal-title":"Nutrients"},{"issue":"03","key":"10.1016\/j.jgeb.2026.100695_b0015","first-page":"466","article-title":"OmarAlzubi,Shadi Nashwan,Awad Ramadan,Musab Al-Zghoul,Someah Alangari, \u201cImpact of Data-Augmentation on Brain Tumor Detection using Different YOLO Versions Models\u201d","volume":"21","author":"Ishtaiwi","year":"2024","journal-title":"The International Arab Journal of Information Technology (IAJIT)"},{"issue":"1","key":"10.1016\/j.jgeb.2026.100695_b0020","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nature Medicine"},{"issue":"4","key":"10.1016\/j.jgeb.2026.100695_b0025","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/17538157.2021.1896524","article-title":"Machine learning approaches to constructing predictive models of vitamin D deficiency in a hypertensive population: a comparative study","volume":"46","author":"Garcia Carretero","year":"2021","journal-title":"Informatics for Health and Social Care"},{"issue":"4","key":"10.1016\/j.jgeb.2026.100695_b0030","doi-asserted-by":"crossref","first-page":"616","DOI":"10.3390\/math10040616","article-title":"Predictive ability of machine-learning methods for vitamin D deficiency prediction by anthropometric parameters","volume":"10","author":"Patino-Alonso","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.jgeb.2026.100695_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2021.100519","article-title":"Assessment of the risk factors for vitamin D3 deficiency in chronic hepatitis B patient using the decision tree learning algorithm in Birjand","volume":"23","author":"Osmani","year":"2021","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.1016\/j.jgeb.2026.100695_b0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-023-35838-6","article-title":"Mode of delivery and maternal vitamin D deficiency: an optimized intelligent Bayesian network algorithm analysis of a stratified randomized controlled field trial","volume":"13","author":"Amiri","year":"2023","journal-title":"Scientific Reports"},{"issue":"1145\/3314074","key":"10.1016\/j.jgeb.2026.100695_b0045","article-title":"Performance of statistical models to predict vitamin D levels","volume":"10","author":"Bechrouri","year":"2019","journal-title":"ACM International Conference Proceeding Series. DOI"},{"key":"10.1016\/j.jgeb.2026.100695_b0050","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2024.1327058","article-title":"Machine learning-based prediction of vitamin D deficiency: NHANES 2001\u20132018","volume":"15","author":"Guo","year":"2024","journal-title":"Frontiers in Endocrinology"},{"key":"10.1016\/j.jgeb.2026.100695_b0055","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Machine Learning"},{"key":"10.1016\/j.jgeb.2026.100695_b0060","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.jgeb.2026.100695_b0065","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.jgeb.2026.100695_b0070","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"issue":"1","key":"10.1016\/j.jgeb.2026.100695_b0075","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"2","key":"10.1016\/j.jgeb.2026.100695_b0080","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"6","key":"10.1016\/j.jgeb.2026.100695_b0085","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.jgeb.2026.100695_b0090","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2021). An introduction to statistical learning: with applications in R (2nd ed.). Springer. 10.1007\/978-1-0716-1418-1.","DOI":"10.1007\/978-1-0716-1418-1_2"},{"key":"10.1016\/j.jgeb.2026.100695_b0095","article-title":"Investigation of performance metrics in regression analysis and machine-learning-based prediction models","author":"Plevris","year":"2022","journal-title":"World Congress in Computational Mechanics and ECCOMAS Congress."},{"issue":"6","key":"10.1016\/j.jgeb.2026.100695_b0100","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biometrics Bulletin"},{"key":"10.1016\/j.jgeb.2026.100695_b0105","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Journal of Genetic Engineering and Biotechnology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1687157X26000399?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1687157X26000399?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T15:42:58Z","timestamp":1785166978000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1687157X26000399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":21,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S1687157X26000399"],"URL":"https:\/\/doi.org\/10.1016\/j.jgeb.2026.100695","relation":{},"ISSN":["1687-157X"],"issn-type":[{"value":"1687-157X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Healthcare Predictions: unmasking the strengths and weaknesses of AI models for vitamin D deficiency assessment","name":"articletitle","label":"Article Title"},{"value":"Journal of Genetic Engineering and Biotechnology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jgeb.2026.100695","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc. on behalf of Academy of Scientific Research and Technology.","name":"copyright","label":"Copyright"}],"article-number":"100695"}}