Sex estimation from long bones: a machine learning approach.
Forensic anthropology
Long bones
Machine learning algorithms
Sex prediction
Sexual dimorphism
Statistical models
Journal
International journal of legal medicine
ISSN: 1437-1596
Titre abrégé: Int J Legal Med
Pays: Germany
ID NLM: 9101456
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
23
03
2023
accepted:
27
07
2023
pubmed:
1
8
2023
medline:
1
8
2023
entrez:
1
8
2023
Statut:
ppublish
Résumé
Sex estimation from skeletal remains is one of the crucial issues in forensic anthropology. Long bones can be a valid alternative to skeletal remains for sex estimation when more dimorphic bones are absent or degraded, preventing any estimation from the first intention methods. The purpose of this study was to generate and compare classification models for sex estimation based on combined measurement of long bones using machine learning classifiers. Eighteen measurements from four long bones (radius, humerus, femur, and tibia) were taken from a total of 2141 individuals. Five machine learning methods were employed to predict the sex: a linear discriminant analysis (LDA), penalized logistic regression (PLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The different classification algorithms using all bones generated highly accuracy models with cross-validation, ranging from 90 to 92% on the validation sample. The classification with isolated bones ranked between 83.3 and 90.3% on the validation sample. In both cases, random forest stands out with the highest accuracy and seems to be the best model for our investigation. This study upholds the value of combined long bones for sex estimation and provides models that can be applied with high accuracy to different populations.
Identifiants
pubmed: 37526736
doi: 10.1007/s00414-023-03072-4
pii: 10.1007/s00414-023-03072-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1887-1895Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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