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

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Abstract

Sex estimation is extremely important in the analysis of human remains as many of the subsequent biological parameters are sex specific (e.g., age at death, stature, and ancestry). When dealing with incomplete or fragmented remains, metric analysis of the tarsal bones of the feet has proven valuable. In this study, the utility of 18 width, length, and height tarsal measurements were assessed for sex-related variation in a Portuguese sample. A total of 300 males and females from the Coimbra Identified Skeletal Collection were used to develop sex prediction models based on statistical and machine learning algorithm such as discriminant function analysis, logistic regression, classification trees, and artificial neural networks. All models were evaluated using 10-fold cross-validation and an independent test sample composed of 60 males and females from the Identified Skeletal Collection of the 21st Century. Results showed that tarsal bone sex-related variation can be easily captured with a high degree of repeatability. A simple tree-based multivariate algorithm involving measurements from the calcaneus, talus, first and third cuneiforms, and cuboid resulted in 88.3 % correct sex estimation both on training and independent test sets. Traditional statistical classifiers such as the discriminant function analysis were outperformed by machine learning techniques. Results obtained show that machine learning algorithm are an important tool the forensic practitioners should consider when developing new standards for sex estimation.

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Acknowledgments

The authors would like to acknowledge Professor Ana Luísa Santos for granting access to the Coimbra Identified Skeletal Collection and Drs. Maria Teresa Ferreira and David Gonçalves for the valuable corrections and suggestions given to the early stages of this manuscript. The authors would also like to thank the anonymous reviewers for their comments and suggestions.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Forensic Sciences Centre (CENCIFOR), Largo da Sé Nova, s/n, 3000-213, Coimbra, Portugal

    David Navega, Ricardo Vicente, Duarte N. Vieira & Eugénia Cunha

  2. Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal

    David Navega, Ricardo Vicente & Eugénia Cunha

  3. Department of Sociology and Anthropology, North Carolina State University, Raleigh, NC, 27695-8107, USA

    Ann H. Ross

  4. Faculty of Medicine, University of Coimbra, Rua Larga, 3004-504, Coimbra, Portugal

    Duarte N. Vieira

Authors
  1. David Navega
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  2. Ricardo Vicente
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  3. Duarte N. Vieira
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  4. Ann H. Ross
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  5. Eugénia Cunha
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Correspondence to David Navega.

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Navega, D., Vicente, R., Vieira, D.N. et al. Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach. Int J Legal Med 129, 651–659 (2015). https://doi.org/10.1007/s00414-014-1070-5

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  • DOI: https://doi.org/10.1007/s00414-014-1070-5

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