Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95721
Title: CADOES: An interactive machine-learning approach for sex estimation with the pelvis
Authors: Coelho, João d'Oliveira 
Curate, Francisco 
Keywords: Forensic Anthropology Population Data; Os Coxa; Sacrum; Supervised learning; Biological Profile
Issue Date: Sep-2019
Publisher: Elsevier
Project: FCT-Pest-OE/SADG/UI0283/2019 
Fundação para a Ciência e Tecnologia SFRH/BD/122306/2016 
Serial title, monograph or event: International Journal of Legal Medicine
Volume: 302
Abstract: The 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.
URI: http://hdl.handle.net/10316/95721
DOI: https://doi.org/10.1016/j.forsciint.2019.109873
Rights: openAccess
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 full item record

Page view(s)

36
checked on Sep 24, 2021

Download(s)

4
checked on Sep 24, 2021

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons