A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium

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dc.authoridOner, Serkan/0000-0002-7802-880X
dc.authoridÖner, Zülal/0000-0003-0459-1015
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authorwosidOner, Serkan/T-2518-2019
dc.authorwosidÖner, Zülal/T-2515-2019
dc.contributor.authorToy, Seyma
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorÖner, Zülal
dc.contributor.authorTuran, Muhammed Kamil
dc.contributor.authorOner, Serkan
dc.contributor.authorSenol, Deniz
dc.date.accessioned2023-03-22T19:47:20Z
dc.date.available2023-03-22T19:47:20Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractThe aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.en_US
dc.identifier.doi10.1038/s41598-022-07415-w
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid35277536en_US
dc.identifier.scopus2-s2.0-85126211508en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-022-07415-w
dc.identifier.urihttps://hdl.handle.net/20.500.14034/626
dc.identifier.volume12en_US
dc.identifier.wosWOS:000767887100020en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.journalScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDimorphismen_US
dc.subjectSkullen_US
dc.titleA study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the craniumen_US
dc.typeArticleen_US

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