Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95721
DC FieldValueLanguage
dc.contributor.authorCoelho, João d'Oliveira-
dc.contributor.authorCurate, Francisco-
dc.date.accessioned2021-09-13T09:46:09Z-
dc.date.available2021-09-13T09:46:09Z-
dc.date.issued2019-09-
dc.identifier.urihttp://hdl.handle.net/10316/95721-
dc.description.abstractThe pelvis is consistently regarded as the most sexually dimorphic region of the human skeleton, and methods for sex estimation with the pelvic bones are usually very accurate. In this investigation, population-specific osteometric models for the assessment of sex with the pelvis were designed using a dataset provided by J.A. Serra (1938) that included 256 individuals (131 females and 125 males) from the Coimbra Identified Skeletal Collection and 38 metric variables. The models for sex estimation were operationalized through an online application and decision support system, CADOES. Different classification algorithms generated high accuracy models, ranging from 85% to 92%, with only three variables; and from 85.33% to 97.33%, with all 38 variables. CADOES conveys a probabilistic prediction of skeletal sex, as well as a suite of attributes with educational applicability in the fields of human skeletal anatomy and statistics. This study upholds the value of the pelvis for the estimation of skeletal sex and provides models for that can be applied with high accuracy and low bias.pt
dc.language.isoengpt
dc.publisherElsevierpt
dc.relationFCT-Pest-OE/SADG/UI0283/2019pt
dc.relationFundação para a Ciência e Tecnologia SFRH/BD/122306/2016pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt
dc.subjectForensic Anthropology Population Datapt
dc.subjectOs Coxapt
dc.subjectSacrumpt
dc.subjectSupervised learningpt
dc.subjectBiological Profilept
dc.titleCADOES: An interactive machine-learning approach for sex estimation with the pelvispt
dc.typearticle-
degois.publication.firstPage109873pt
degois.publication.titleInternational Journal of Legal Medicinept
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0379073819302890pt
dc.peerreviewedyespt
dc.identifier.doihttps://doi.org/10.1016/j.forsciint.2019.109873pt
degois.publication.volume302pt
dc.date.embargo2019-09-01*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCIAS - Research Centre for Anthropology and Health-
crisitem.author.orcid0000-0003-0871-1926-
crisitem.author.orcid0000-0002-0480-209X-
Appears in Collections:I&D CIAS - Artigos em Revistas Internacionais
Files in This Item:
File Description SizeFormat
Coelho e Curate 2019.pdf339.09 kBAdobe PDFView/Open
Show simple item record

Page view(s)

106
checked on Nov 21, 2022

Download(s)

132
checked on Nov 21, 2022

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons