A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals

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dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authoridOner, Serkan/0000-0002-7802-880X
dc.contributor.authorTuran, Muhammed Kamil
dc.contributor.authorOner, Zulal
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorOner, Serkan
dc.date.accessioned2024-09-29T15:55:11Z
dc.date.available2024-09-29T15:55:11Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground: Predicting sex is an important problem in forensic medicine. The femur, patella, mandible and calcaneus bones are frequently used in predicting sex. In our study, we aimed to use the artificial neural network (ANN) technique to predict sex by measuring the values of the phalanges of the first and fifth toes and the first and fifth metatarsal bones. Method: All bone measurements were conducted on the direct X-ray images of 176 males and 178 females in the age range of 24-60 years. The multilayer perceptron classifier (MLPC) input layer included parameters on the bone length measurements of phalanx proximalis I, phalanx distalis I, metatarsal I, phalanx proximalis V, phalanx medialis V, phalanx distalis V and metatarsal V. The output layer contained two neurons to define the male and female sexes. The present study used an MLPC model that had two hidden layers, and the first and second hidden layers contained 14 and 7 nodes, respectively. Results: The model had an overall accuracy (Acc) of 0.95, specificity (Spe) of 0.97, sensitivity (Sen) of 0.95 and Matthews correlation coefficient (Mcc) of 0.92. While the sex prediction success of our proposed model was higher in women, the results were more specific in men and more sensitive in women (Acc(male) = 0.93, Acc(Female) = 0.98, Sen(male) = 0.93, Spe(male) = 0.98, Sen(Female) = 0.98 and Spe(Female) = 0.93). Conclusions: This study demonstrated that the ANN model for length measurements on small bones is a highly effective instrument for sex prediction.en_US
dc.identifier.doi10.1016/j.compbiomed.2019.103490
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid31606585en_US
dc.identifier.scopus2-s2.0-85072970257en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2019.103490
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4510
dc.identifier.volume115en_US
dc.identifier.wosWOS:000503085900004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPhalanxen_US
dc.subjectMetatarsalen_US
dc.subjectX-rayen_US
dc.subjectArtificial neural networken_US
dc.subjectMultilayer perceptron classifieren_US
dc.subjectSex identificationen_US
dc.titleA trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsalsen_US
dc.typeArticleen_US

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