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Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers

2017Journal article Restricted access
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dc.contributor.authorCurate, Francisco
dc.contributor.authorUmbelino, Cláudia
dc.contributor.authorPerinha, A.
dc.contributor.authorNogueira, C.
dc.contributor.authorSilva, Ana Maria
dc.contributor.authorCunha, Eugénia
dc.date.accessioned2019-02-05T14:03:48Z
dc.date.available2019-02-05T14:03:48Z
dc.date.issued2017
dc.description.abstractThe assessment of sex is of paramount importance in the establishment of the biological profile of a skeletal individual. Femoral relevance for sex estimation is indisputable, particularly when other exceedingly dimorphic skeletal regions are missing. As such, this study intended to generate population-specific osteometric models for the estimation of sex with the femur and to compare the accuracy of the models obtained through classical and machine-learning classifiers. A set of 15 standard femoral measurements was acquired in a training sample (100 females; 100 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal) and models for sex classification were produced with logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM), and reduce error pruning trees (REPTree). Under cross-validation, univariable sectioning points generated with REPTree correctly estimated sex in 60.0-87.5% of cases (systematic error ranging from 0.0 to 37.0%), while multivariable models correctly classified sex in 84.0-92.5% of cases (bias from 0.0 to 7.0%). All models were assessed in a holdout sample (24 females; 34 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with an allocation accuracy ranging from 56.9 to 86.2% (bias from 4.4 to 67.0%) in the univariable models, and from 84.5 to 89.7% (bias from 3.7 to 23.3%) in the multivariable models. This study makes available a detailed description of sexual dimorphism in femoral linear dimensions in two Portuguese identified skeletal samples, emphasizing the relevance of the femur for the estimation of sex in skeletal remains in diverse conditions of completeness and preservation.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCurate, F., Umbelino, C., Perinha, A., Nogueira, C., Silva, A. M., & Cunha, E. (2017). Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers. J Forensic Leg Med, 52 75-81. doi: 10.1016/j.jflm.2017.08.011pt_PT
dc.identifier.doi10.1016/j.jflm.2017.08.011pt_PT
dc.identifier.issn1878-7487
dc.identifier.issn1752-928X
dc.identifier.urihttp://hdl.handle.net/10451/36862
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1752928X17301257?via%3Dihubpt_PT
dc.subjectAdultpt_PT
dc.subjectAgedpt_PT
dc.subjectAged, 80 and overpt_PT
dc.subjectDiscriminant analysispt_PT
dc.subjectFemalept_PT
dc.subjectFemurpt_PT
dc.subjectForensic anthropologypt_PT
dc.subjectHumanspt_PT
dc.subjectLogistic modelspt_PT
dc.subjectMalept_PT
dc.subjectMiddle agedpt_PT
dc.subjectPortugalpt_PT
dc.subjectSex Determination by Skeletonpt_PT
dc.subjectYoung Adultpt_PT
dc.subjectMachine learningpt_PT
dc.titleSex determination from the femur in Portuguese populations with classical and machine-learning classifierspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F74015%2F2010/PT
oaire.citation.endPage81pt_PT
oaire.citation.startPage75pt_PT
oaire.citation.titleJournal of Forensic and Legal Medicinept_PT
oaire.citation.volume52pt_PT
oaire.fundingStreamSFRH
person.familyNameSilva
person.givenNameAna Maria
person.identifier.ciencia-idF21D-4659-29AB
person.identifier.orcid0000-0002-1912-6581
person.identifier.ridE-6281-2015
person.identifier.scopus-author-id55939389400
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery2bd9c836-5465-4034-afd9-f3d50381488c
relation.isProjectOfPublicatione82b0903-20b5-41f1-8f2b-c27c19b0e952
relation.isProjectOfPublication.latestForDiscoverye82b0903-20b5-41f1-8f2b-c27c19b0e952

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