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Selecting best-fit models for estimating the body mass from 3D data of the human calcaneus

Title
Selecting best-fit models for estimating the body mass from 3D data of the human calcaneus
Authors
Jung, Go-UnLee, U-YoungKim, Dong-HoKwak, Dai-SoonAhn, Yong-WooHan, Seung-HoKim, Yi-Suk
Ewha Authors
김이석
SCOPUS Author ID
김이석scopus
Issue Date
2016
Journal Title
FORENSIC SCIENCE INTERNATIONAL
ISSN
0379-0738JCR Link1872-6283JCR Link
Citation
vol. 262, pp. 37 - 45
Keywords
CalcaneusBody mass estimationAll-possible-regressionThree-dimensional modelKorean populationForensic Anthropology Population data
Publisher
ELSEVIER IRELAND LTD
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
Body mass (BM) estimation could facilitate the interpretation of skeletal materials in terms of the individual's body size and physique in forensic anthropology. However, few metric studies have tried to estimate BM by focusing on prominent biomechanical properties of the calcaneus. The purpose of this study was to prepare best-fit models for estimating BM from the 3D human calcaneus by two major linear regression analysis (the heuristic statistical and all-possible-regressions techniques) and validate the models through predicted residual sum of squares (PRESS) statistics. A metric analysis was conducted based on 70 human calcaneus samples (29 males and 41 females) taken from 3D models in the Digital Korean Database and 10 variables were measured for each sample. Three best-fit models were postulated by F-statistics, Mallows' C-p, and Akaike information criterion (AIC) and Bayes information criterion (BIC) for each available candidate models. Finally, the most accurate regression model yields lowest % SEE and 0.843 of R-2. Through the application of leave-one-out cross validation, the predictive power was indicated a high level of validation accuracy. This study also confirms that the equations for estimating BM using 3D models of human calcaneus will be helpful to establish identification in forensic cases with consistent reliability. (C) 2016 Published by Elsevier Ireland Ltd.
DOI
10.1016/j.forsciint.2016.01.022
Appears in Collections:
의학전문대학원 > 의학과 > Journal papers
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