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Sex estimation from the greater sciatic notch: a comparison of classical statistical models and machine learning algorithms

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Abstract

The greater sciatic notch (GSN) is a useful element for sex estimation because it is quite resistant to damage, and thus it can often be assessed even in poorly preserved skeletons. This study aimed to develop statistical models for sex estimation based on visual and metric analyses of the GSN, and additional variables linked to the GSN. A total of 60 left coxal bones (30 males and 30 females) were analysed. Fifteen variables were measured, and one was a morphologic variable. These 16 variables were used for the comparison of six statistical models: linear discriminant analysis (LDA), regularized discriminant analysis (RDA), penalized logistic regression (PLR) and flexible discriminant analysis (FDA), and two machine learning algorithms, support vector machine (SVM) and artificial neural network (ANN). The statistical models were built in two steps: firstly, only with the GSN variables (group 1), and secondly, with the whole variables (group 2), in order to see if the models including all the variables performed better. The overall accuracy of the models was very close, ranging from 0.92 to 0.97 using specific GSN variables. When additional variables starting from the deepest point of GSN are available, it is worth to use them, because the accuracy increases. PLR (after optimization of parameters) stands out from other statistical models. The position of the deepest point of GSN (Fig. 2) probably plays a crucial role for the sexual dimorphism, as stated by the good performance of the visual assessment of this point and the fact that the A2 angle (posterior angle with the deepest point of the GSN as the apex) is included in all models.

Highlights

  • Morphologic and metric evaluation of the greater sciatic notch from a contemporary French sample.

  • Comparison of six non-classical statistical models.

  • Plain morphologic assessment allows for nearly 92% of correct classification.

  • Accuracy ranges between 0.92 and 0.97 and 0.93 and 0.99 for targeted variables and all variables (targeted and non-targeted), respectively.

  • PLR with an optimization process gives the best accuracy with targeted variables.

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Acknowledgements

We wish to thank Marie-Catherine Francino for proofreading this article.

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Authors and Affiliations

  1. Faculté de Médecine, Université de Lorraine, 9 avenue de la Forêt de Haye, 54500, Vandœuvre-les-Nancy, France

    Siam Knecht

  2. Faculté de Médecine, Institut Universitaire d’Anthropologie Médico-Légale Université Côte d’Azur, 28 Avenue de Valombrose, 06107 cedex 2, Nice, France

    Siam Knecht, Luísa Nogueira, Maël Servant, Véronique Alunni, Caroline Bernardi & Gérald Quatrehomme

  3. UMR 5199 PACEA, Université de Bordeaux, Allée Geoffroy Saint-Hilaire, Bâtiment B8, CS 50023, Cedex 33615, Pessac, France

    Frédéric Santos

  4. CEPAM, UMR CNRS 7264, 24 avenue des Diables Bleus, 06300, Nice, France

    Véronique Alunni, Caroline Bernardi & Gérald Quatrehomme

Authors
  1. Siam Knecht
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  2. Luísa Nogueira
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  3. Maël Servant
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  4. Frédéric Santos
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  5. Véronique Alunni
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  6. Caroline Bernardi
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  7. Gérald Quatrehomme
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Contributions

Siam Knecht: conceptualization, methodology, investigation, writing, and editing.

Luisa Nogueira: conceptualization, methodology, investigation, writing, and editing.

Maël Servant: reviewing and editing.

Fréderic Santos: methodology, reviewing, and editing.

Véronique Alunni: reviewing and editing.

Caroline Bernardi: reviewing and editing.

Gérald Quatrehomme: conceptualization, methodology, writing, reviewing, and editing.

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Correspondence to Siam Knecht.

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Knecht, S., Nogueira, L., Servant, M. et al. Sex estimation from the greater sciatic notch: a comparison of classical statistical models and machine learning algorithms. Int J Legal Med 135, 2603–2613 (2021). https://doi.org/10.1007/s00414-021-02700-1

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