Machine learning algorithms for sex classification by using variables of orbital structures: A computed tomography study

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dc.authorid0000-0002-9218-6468
dc.authorid0000-0001-5587-1055
dc.authorid0000-0002-0417-1806
dc.contributor.authorŞenol, Gamze Taşkın
dc.contributor.authorKürtül, İbrahim
dc.contributor.authorRay, Abdullah
dc.contributor.authorRay, Gülçin
dc.date.accessioned2024-09-25T19:59:59Z
dc.date.available2024-09-25T19:59:59Z
dc.date.issued2024
dc.departmentBAİBÜ, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü en_US
dc.description.abstractSince machine learning algorithms give more reliable results, they have been used in the field of health in recent years. The orbital variables give very successful results in classifying sex correctly. This research has focused on sex determinationusing certain variables obtained from the orbital images of the computerized tomography (CT) by using machine learning algorithms (ML).is In study th 12 variables determined on 600 orbital images of 300 individuals (150 men and 150 women) were tested with different ML. ree Decision (DT), t algorithms of ML were used for unsupervised learning. Statistical analyses of the variables were conducted with Minitab (R) 64-bit)21.2 ( program. ACC rate of NB, DT, KNN, and LR algorithms was found as % 83 while the ACC rate of LDA and RFC algorithms was determind as % 85. According to Shap analysis, the variable with the highest degree of effect was found as BOW. The study has thedetermined sex with high accuracy at the ratios of 0.83 and 0.85 through using the variables of the orbital CT images, and the related morphometricdata of the population under question was acquired, emphasizing the racial variation.en_US
dc.identifier.endpage976en_US
dc.identifier.issn0717-9502
dc.identifier.issn0717-9367
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85203080201en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage970en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14015
dc.identifier.volume42en_US
dc.identifier.wosWOS:001308286200013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorid0000-0002-9218-6468
dc.institutionauthorid0000-0001-5587-1055
dc.institutionauthorid0000-0002-0417-1806
dc.language.isoenen_US
dc.publisherSoc Chilena Anatomiaen_US
dc.relation.ispartofInternational Journal of Morphologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectSex Determinationen_US
dc.subjectMachine Learningen_US
dc.subjectOrbital Apertureen_US
dc.subjectThree-Dimensional Computed Tomographyen_US
dc.subjectEmphasizing the Racial Variation
dc.subjectLR Algorithms
dc.titleMachine learning algorithms for sex classification by using variables of orbital structures: A computed tomography studyen_US
dc.typeArticleen_US

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