Gender prediction using parameters obtained from the dens axis with machine learning algorithms and artificial neural networks
Yazarlar (6)
Öğr. Gör. Dr. Oğuzhan HARMANDAOĞLU Kastamonu Üniversitesi, Türkiye
Yusuf Seçgin Karabük Üniversitesi, Türkiye
Seren Kaya
Türkiye
Türkiye
Öğr. Gör. Oğuzhan ÖZTÜRK Kastamonu Üniversitesi, Türkiye
Doç. Dr. Deniz Şenol Düzce Üniversitesi, Türkiye
Prof. Dr. Ömer Önbaş Düzce Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | IRANIAN RED CRESCENT MEDICAL JOURNAL (Q4) | ||
| Dergi ISSN | 2074-1804 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Cilt / Sayı / Sayfa | 27 / 1 / 1–8 | DOI | 10.22034/ircmj.2025.490322.1636 |
| Makale Linki | https://www.ircmj.com/article_217608_fe6644f184d28ae06ac5e3d4b0ce3021.pdf | ||
| UAK Araştırma Alanları |
Anatomi
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| Özet |
| Background and Objectives: Due to the difficulties associated with the separation, damage, cremation, and commingling of skeletal remains, it is of great importance in forensic medicine to assess the accuracy and reliability of sex estimates derived from different skeletal components. For this purpose, this study aimed to classify gender using machine learning (ML) algorithms and a multilayer perceptron classifier (MLPC) based on morphometric data of the dens axis obtained from computed tomography (CT) images. Methods: Retrospectively, measurements were taken from CT images of 300 male and 300 female individuals aged between 18–65 years, including dens axis height (DAH), anteroposterior (APDDA) and anterosuperior lengths (ASDDA), dens axis angle (DAA), clivodental angle (CDA), and Boogard angle (BOO). Machine learning models such as Extra Tree Classifier (ETC), Random Forest (RF), Decision Tree (DT), Gaussian Naive Bayes (GaussianNB), k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Logistic Regression (LR) were used. MLPC was chosen as artificial neural networks (ANN) model.Results: Significant differences were found between genders in all dens axis parameters except BOO (p< 0.05). The highest accuracy rate in ML algorithm modeling was found to be 0.80 with LDA, RF, k-NN algorithms, and MLPC. The parameter with the highest impact on gender classification was the dens axis anterosuperior length.Conclusion: It was found that the parameters obtained from the dens axis using MLCP and ML algorithms have sufficient accuracy rates the … |
| Anahtar Kelimeler |
| Dens Axis | Odontoid Process | Artificial Neural Networks | Machine Learning Algorithms | Gender Prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Google Scholar | 4 |