Can insulin resistance be predicted with vitamin B12, ferritin, vitamin D and demographic information using machine learning algorithms?

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dc.contributor.authorYigit, Meltem
dc.contributor.authorSeçgın, Yusuf
dc.contributor.authorOner, Zulal
dc.contributor.authorOlukman, Ozgur
dc.date.accessioned2025-03-20T09:41:20Z
dc.date.available2025-03-20T09:41:20Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractIn this study, we aimed to investigate the relationship between insulin resistance and vitamin D, vitamin B12 and ferritin levels. Between January 2022 and October 2023, 322 patients (133 females, 189 males) between the ages of 3-16 years who were admitted to the Pediatrics outpatient clinics of İzmir Bakırçay University Çiğli Training and Research Hospital were included. Pediatric patients with no known chronic disease and measured vitamin B12, vitamin D, ferritin and HOMA- IR levels were retrospectively included in the study. Individuals with HOMA-IR<2.5 were considered as (normal) controls and those with HOMA-IR≥2.5 were considered as insulin resistant children. HOMA-IR groups were predicted with an accuracy rate of 0.80 with the Random Forest (RF) algorithm, one of the machine learning algorithms. In addition, the highest contribution of RF to the determination of HOMA-IR with the SHAP analyzer was found to be provided by age, followed by vitamin B12. The results of the study revealed that vitamin B12, vitamin D, ferritin level and age are important factors on insulin resistance. Early vitamin D, vitamin B12 and ferritin replacement is important for the control of metabolic diseases in the future.
dc.identifier.doi10.5455/medscience.2023.11.221
dc.identifier.endpage60
dc.identifier.issn2147-0634
dc.identifier.issue1
dc.identifier.startpage55
dc.identifier.trdizinid1244581
dc.identifier.urihttps://doi.org/10.5455/medscience.2023.11.221
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1244581
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1908
dc.identifier.volume13
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMedicine Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250319
dc.subjectTıbbi İnformatik
dc.subjectEndokrinoloji ve Metabolizma
dc.subjectBiyokimya ve Moleküler Biyoloji
dc.subjectPediatri
dc.titleCan insulin resistance be predicted with vitamin B12, ferritin, vitamin D and demographic information using machine learning algorithms?
dc.typeArticle

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