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A Generalised Neural Network Model to Estimate Sex from Cranial Metric Traits: A Robust Training and Testing Approach

  • Identification data

    Identifier: imarina:9282174
  • Authors:

    Del Bove, A
    Veneziano, A
  • Others:

    Author, as appears in the article.: Del Bove, A; Veneziano, A
    Department: Història i Història de l'Art
    URV's Author/s: Del Bove, Antonietta
    Keywords: Skulls Sexual dimorphism Sample Protocol Populations Physical anthropology Neural network analysis Human cranium Dimorphism Age Accuracy
    Abstract: Featured Application The method presented can be used to estimate sex attributes from a small set of cranial metric traits. The morphology of the human cranium allows for reconstructing important information about the identity of an individual, such as age, ancestry, sex, and health status. The estimation of sex from morphology is a key component of the work of physical anthropologists, and in the last decade, the field has witnessed an increase in the use of novel algorithm-based methodologies to tackle the aforementioned task. Nevertheless, several limitations (e.g., small training/testing sample size, training-test data relatedness, limited population inclusiveness, overfitting) have hampered the application of such methods as a standardised procedure in the field. Here, we propose a population-inclusive protocol for estimating sex from a small set of cranial metric traits (10 measurements) based on a neural network architecture trained to maximise the probability of sex attribution and prevent overfitting. The cross-validation returned an accuracy of 86.7% +/- 0.02% and log loss of 0.34 +/- 0.03. The protocol developed was tested on data unrelated to that of the training and validation phase and returned an estimated accuracy of 84.3% and log loss of 0.348. The model and the related code to use it are made publicly available.
    Thematic Areas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: antonietta.delbove@urv.cat antonietta.delbove@urv.cat
    Record's date: 2024-05-23
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2076-3417/12/18/9285
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Applied Sciences-Basel. 12 (18):
    APA: Del Bove, A; Veneziano, A (2022). A Generalised Neural Network Model to Estimate Sex from Cranial Metric Traits: A Robust Training and Testing Approach. Applied Sciences-Basel, 12(18), -. DOI: 10.3390/app12189285
    Article's DOI: 10.3390/app12189285
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Chemistry, Multidisciplinary,Computer Science Applications,Engineering (Miscellaneous),Engineering, Multidisciplinary,Fluid Flow and Transfer Processes,Instrumentation,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Physics, Applied,Process Chemistry and Technology
    Skulls
    Sexual dimorphism
    Sample
    Protocol
    Populations
    Physical anthropology
    Neural network analysis
    Human cranium
    Dimorphism
    Age
    Accuracy
    Química
    Process chemistry and technology
    Physics, applied
    Materials science, multidisciplinary
    Materials science (miscellaneous)
    Materials science (all)
    Materiais
    Instrumentation
    General materials science
    General engineering
    Fluid flow and transfer processes
    Engineering, multidisciplinary
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias ii
    Engenharias i
    Computer science applications
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência de alimentos
    Chemistry, multidisciplinary
    Biodiversidade
    Astronomia / física
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