Sex prediction using machine learning algorithms with parameters obtained from CT images of the Canaliculi pterygoidei
Küçük Resim Yok
Tarih
2026
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, our aim is to predict sex using machine learning (ML) algorithms with morphometric variables taken from the vidian canal. The study was performed on Computed Tomography (CT) images of 137 women and 137 men aged 18-65 years. Seventeen morphometric variables were determined and recorded from axial and coronal skull images. These numerical data were used for sex determination with ML algorithms. As a result of the ML algorithm analysis of the data as 80% training set and 20% test set, the highest Accuracy (Acc.) rate was 0.89 with the Extra Tree Classifier (ETC) algorithm, and the lowest was 0.78 with Quadratic Discriminant Analysis (QDA). Among the parameters, the distance between the optic canal and the vidian canal was found to have the highest effect on sex determination with the Shapley Additive Explanations analyser. As a result of 10-fold cross-validation analysis of ML algorithms, the highest Acc rate was 0.82 with Random Forest (RF) algorithm, and the lowest was 0.73 with QDA. The findings of the study reveal that morphometric variables from the vidian canal show high accuracy in sex determination.
Açıklama
Anahtar Kelimeler
[Keyword Not Available]
Kaynak
Australian Journal of Forensic Sciences
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
