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Statistical classification methods for estimating sex based on five skull traits: A nonmetric assessment using 3D CT models

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Author(s)
Yun Taek ShimDeog-Im KimNahyun AumSeung Gyu ChoiYoung Seok LeeHyung Nam KooYi-Suk Kim
Keimyung Author(s)
Kim, Deog Im
Department
Dept. of Nursing (간호학)
Journal Title
Homo
Issued Date
2023
Volume
74
Issue
1
Abstract
Five cranial nonmetric traits for sex estimation for sex estimation are classified by score according to geometry. The population of origin is one of the factors influencing cranial nonmetric traits. Moreover, among the five cranial traits, the robust traits for estimating sex varied across population. The aim of this study is to suggest the most useful method for sex estimation and demonstrate the need of a suitable method for each population. One-hundred thirty-five three-dimensional skull images from 21st century Korean autopsy cadavers were evaluated using the ordinal scoring system of five cranial nonmetric traits as outlined in Buikstra & Ubelaker (1994). All scores of each trait were analyzed by linear discriminant and decision tree analyses for sex estimation. The frequency of each trait was analyzed and compared to populations from other studies. The accuracy for both sexes was 88.1% by discriminant analysis and 90.4% by decision tree. The traits with the highest accuracy were the glabella and mastoid process in both discriminant analysis and decision tree. Sex estimation in modern Korean cadavers using the cranial nonmetric method was shown to be highly accurate by both discriminant analysis and decision tree. When comparing the pattern of frequency scores in this study with those of other populations, the pattern of trait scores for estimating sex was different for each population, even among populations in the same Asian region, which suggests the need for methods suited for specific populations.
Keimyung Author(s)(Kor)
김덕임
Publisher
College of Nursing (간호대학)
Type
Article
ISSN
1618-1301
Source
https://www.schweizerbart.de/papers/homo/detail/prepub/102751/Statistical_classification_methods_for_estimating_?af=crossref
DOI
10.1127/homo/2023/1632
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44979
Appears in Collections:
2. College of Nursing (간호대학) > Dept. of Nursing (간호학)
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