Applying data mining techniques to predict vitamin D deficiency in diabetic patients

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dc.authoridSarac, Esra/0000-0002-2503-0084
dc.authoridESSIZ, ENGIN/0000-0002-4062-3443
dc.authoridYuregir, Oya H./0000-0002-9607-8149
dc.contributor.authorEssiz, Ugur Engin
dc.contributor.authorYuregir, Oya Hacire
dc.contributor.authorSarac, Esra
dc.date.accessioned2025-01-06T17:37:29Z
dc.date.available2025-01-06T17:37:29Z
dc.date.issued2023
dc.description.abstractVitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.
dc.identifier.doi10.1177/14604582231214864
dc.identifier.issn1460-4582
dc.identifier.issn1741-2811
dc.identifier.issue4
dc.identifier.pmid37963409
dc.identifier.scopus2-s2.0-85177064194
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1177/14604582231214864
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2230
dc.identifier.volume29
dc.identifier.wosWOS:001101892000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofHealth Informatics Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectdiabetes
dc.subjectlogistic regression
dc.subjectrandom forest
dc.subjectrelief-F
dc.subjectsupport vector machines
dc.subjectvitamin D
dc.titleApplying data mining techniques to predict vitamin D deficiency in diabetic patients
dc.typeArticle

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