Literature DB >> 22440582

The age at death assessment in a multi-ethnic sample of pelvic bones using nature-inspired data mining methods.

Zdenek Buk1, Pavel Kordik, Jaroslav Bruzek, Aurore Schmitt, Miroslav Snorek.   

Abstract

Recently published studies showed that age assessment methods are population specific. Authors analyse the senescence changes in pubic symphysis and sacro-pelvic surface of a pelvic bone using data mining methods. The multi-ethnic data set consists of 956 adult individuals ranging from 19 to 100 years of age derived from 9 different populations with known age and sex. The results show that accurate and reliable age assessment is possible to three age classes (less than 30, 30-60, 60 and more). The study confirms that population specificity of the methods exists and the variable "sex" is not important in age classification.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Mesh:

Year:  2012        PMID: 22440582     DOI: 10.1016/j.forsciint.2012.02.019

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  4 in total

1.  Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach.

Authors:  David Navega; Ricardo Vicente; Duarte N Vieira; Ann H Ross; Eugénia Cunha
Journal:  Int J Legal Med       Date:  2014-09-04       Impact factor: 2.686

2.  Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis.

Authors:  David Navega; Ernesto Costa; Eugénia Cunha
Journal:  Biology (Basel)       Date:  2022-03-30

3.  Age Classification in Forensic Medicine Using Machine Learning Techniques.

Authors:  G V Zolotenkova; A I Rogachev; Y I Pigolkin; I S Edelev; V N Borshchevskaya; R Cameriere
Journal:  Sovrem Tekhnologii Med       Date:  2022-01-28

4.  The computational age-at-death estimation from 3D surface models of the adult pubic symphysis using data mining methods.

Authors:  Anežka Kotěrová; Michal Štepanovský; Zdeněk Buk; Jaroslav Brůžek; Nawaporn Techataweewan; Jana Velemínská
Journal:  Sci Rep       Date:  2022-06-20       Impact factor: 4.996

  4 in total

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