Determination of Gender By Machine Learning Algorithms, Through Using Craniocervical Junction Parameters and Dimensions of the Cervical Spinal Canal

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dc.contributor.authorŞENOL, Gamze TAŞKIN
dc.contributor.authorKÜRTÜL, İbrahim
dc.contributor.authorRAY, Abdullah
dc.contributor.authorAHMETOĞLU, Gülçin
dc.contributor.authorSEÇGİN, Yusuf
dc.contributor.authorÖNER, Zülal
dc.date.accessioned2024-03-09T20:43:11Z
dc.date.available2024-03-09T20:43:11Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractGender determination is the first step for biological identification. With the widespread use of machine learning algorithms (MLA) for diagnosis, the significance of applying them also in gender determination studies has become apparent. This study has therefore aimed at determining gender from the parameters obtained out of magnetic resonance images (MRI) of the cranio-cervical junction and cervical-spinal canal by using MLA. MRI of the craniocervical junction and cervical-spinal canal of 110 men and 110 women were included in this study. The 15 parameters were tested with Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews-correlation coefficient (Mcc) values were used as performance criteria. The Acc, Spe, Sen, F1, and Mcc were found to be 1.00 in the LR, LDA, QDA and RF algorithms. The ratios of the Acc, Spe, Sen, and F1 were 0.98, and of the Mcc was 0.96 in the DT algorithm. It was found that the ratio between the SHAP analyzer of the RF algorithm and the belt of the ratio between the arch of the atlas and the anterior-posterior distance of the dens (R3) parameter had a higher contribution to the estimation of gender compared to other parameters. It was concluded that the LDA, QDA, LR, DT and RF algorithms applied to the parameters acquired from the MRI of the craniocervical junction and cervical-spinal canal, could determine the gender with very high accuracy.en_US
dc.identifier.doi10.20515/otd.1291030
dc.identifier.endpage677en_US
dc.identifier.issn1305-4953
dc.identifier.issn2587-1579
dc.identifier.issue5en_US
dc.identifier.startpage672en_US
dc.identifier.trdizinid1198756en_US
dc.identifier.urihttps://doi.org/10.20515/otd.1291030
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1198756
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1675
dc.identifier.volume45en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofOsmangazi Tıp Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleDetermination of Gender By Machine Learning Algorithms, Through Using Craniocervical Junction Parameters and Dimensions of the Cervical Spinal Canalen_US
dc.typeArticleen_US

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