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Journals / Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi / 2026 / Volume: 11 Issue: 3

Gender Prediction From Foramen Magnum Using Machine Learning Algorithms

Nesibe Yılmaz(KARABUK UNIVERSITY)0000-0002-5527-8507
Yusuf Seçgin(KARABUK UNIVERSITY)0000-0002-0118-6711
Rabia Demirci(KARABUK UNIVERSITY)0009-0002-0159-1909
Nevin Köremezli Keskin(KARABUK UNIVERSITY)0000-0002-3169-9083
Pages
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Publication Date
2024-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, it was found that the for. magnum height parameter contributed the most to this high rate with the SHapley Additive exPlanations analyzer.

📌 3 Key Takeaways

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Objective & Hypothesis:Aim; Accurate and highly accurate postmortem identification of the individual is important for forensic sciences.
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Key Findings & Data:Method; The study was performed on CT images of 214 individuals aged 18-65 years.
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Conclusion & Impact:In addition, it was found that the for. magnum height parameter contributed the most to this high rate with the SHapley Additive exPlanations analyzer.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Foramen magnum · computed tomography · machine learning algorithms

Abstract

Aim; Accurate and highly accurate postmortem identification of the individual is important for forensic sciences. The main element of identification is the determination of gender. The aim of this study is to predict gender with high accuracy using Machine learning (ML) algorithms with parameters of the foramen magnum obtained from Computed Tomography (CT) images. Method; The study was performed on CT images of 214 individuals aged 18-65 years. For. magnum length, width, circumference and area were measured on the images. The measurements were used in ML algorithms for gender prediction and performance values were recorded. Results; As a result of the study, gender prediction results with high accuracy up to 0.84 were obtained with ML algorithms. In addition, it was found that the for. magnum height parameter contributed the most to this high rate with the SHapley Additive exPlanations analyzer. Conclusion; As a result of our study, it was found that the parameters obtained from for. magnum can be used for sex prediction in the Turkish population by analyzing them with ML algorithm. In this respect, we think that it will contribute to other metric studies in the Turkish population.

Özet

Giriş ve Amaç: Bireyin ölüm sonrası doğru ve yüksek doğrulukta kimliklendirilmesi adli bilimler için önemlidir. Kimlik tespitinin ana unsuru ise cinsiyetin belirlenmesidir. Bu çalışmanın amacı, Bilgisayarlı Tomografi (BT) görüntülerinden elde edilen foramen magnum parametreleri ile Makine öğrenimi (ML) algoritmalarını kullanarak cinsiyeti yüksek doğrulukla tahmin etmektir. Gereç ve Yöntemler: Çalışma 18-65 yaş arası 214 bireyin BT görüntüleri üzerinde gerçekleştirilmiştir. Görüntüler üzerinde for. magnum uzunluğu, genişliği, çevresi ve alanı ölçülmüştür. Ölçümler cinsiyet tahmini için ML algoritmalarında kullanıldı ve performans değerleri kaydedildi. Bulgular: Çalışma sonucunda ML algoritmaları ile 0,84'e varan yüksek doğrulukta cinsiyet tahmin sonuçları elde edilmiştir. Ayrıca SHapley Additive exPlanations analizörü ile bu yüksek orana en çok for. magnum yükseklik parametresinin katkı sağladığı tespit edilmiştir. Sonuç: Çalışmamız sonucunda for. magnum'dan elde edilen parametrelerin ML algoritması ile analiz edilerek Türk popülasyonunda cinsiyet tahmini için kullanılabileceği tespit edilmiştir. Bu açıdan Türk popülasyonunda yapılacak diğer metrik çalışmalarına katkı sağlayacağını düşünüyoruz.

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Cite This Article

Yılmaz, N., Seçgin, Y., Demirci, R., Keskin, N. K. (2024). Gender Prediction From Foramen Magnum Using Machine Learning Algorithms. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, Volume: 11 Issue: 3. https://doi.org/10.34087/cbusbed.1496208

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