Gender Estimation from 2D:4D Ratio and Hand Morphometry by Using Machine Learning Algorithms

Ahmet Kurtoğlu; Rukiye Çiftçi

Makale detayları

Gender Estimation from 2D:4D Ratio and Hand Morphometry by Using Machine Learning Algorithms

Yazarlar:

Ahmet Kurtoğlu | Rukiye Çiftçi

Yayıncı:

Harran University

Ana Başlıklar:

Spor Hekimliği | Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.21 Issue 2, pp.253-259

Belge Tipi:

Makale

Yayın Tarihi:

8/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323553

pISSN:

1304-9623

Anahtar kelimeler:

Özet:

 Background: The present study was conducted to estimate gender from 2D:4D ratio and hand mor-phometry taken from participants by using machine learning ML algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women be-tween the ages of 18 and 30 who did not have any pathology, deformity or surgical interventions on their hands. Hand width HW, hand length HL, second digit length 2D, and fourth digit length 4D of the individuals were measured as the right R and left L side by using digital calliper and recorded in Excel. In addition, the ratio between the second digit and fourth digit 2D:4D of each individual was also recorded. Results: As a result of ML modelling, 0.92 accuracy was obtained with Random forest RF algorithm. With RF algorithm, all of the 16 women and 18 of the 21 men in the test set were estimated accu-rately. With SHAP analyzer of RF algorithm, HW-L parameter was found to have the highest contri-bution in estimating gender. The accuracy rates of the other ML models used in the study were found to vary between 0.78 and 0.89. Conclusions: It was found that 2D:4D ratio and hand morphometry measurements, which are fre-quently preferred in gender determination, have higher accuracy rate when examined with ML algorithms. In our study, we concluded that using 2D:4D ratio and hand morphometry in estimating gender provides accurate and reliable data.

 Background: The present study was conducted to estimate gender from 2D:4D ratio and hand mor-phometry taken from participants by using machine learning ML algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women be-tween the ages of 18 and 30 who did not have any pathology, deformity or surgical interventions on their hands. Hand width HW, hand length HL, second digit length 2D, and fourth digit length 4D of the individuals were measured as the right R and left L side by using digital calliper and recorded in Excel. In addition, the ratio between the second digit and fourth digit 2D:4D of each individual was also recorded. Results: As a result of ML modelling, 0.92 accuracy was obtained with Random forest RF algorithm. With RF algorithm, all of the 16 women and 18 of the 21 men in the test set were estimated accu-rately. With SHAP analyzer of RF algorithm, HW-L parameter was found to have the highest contri-bution in estimating gender. The accuracy rates of the other ML models used in the study were found to vary between 0.78 and 0.89. Conclusions: It was found that 2D:4D ratio and hand morphometry measurements, which are fre-quently preferred in gender determination, have higher accuracy rate when examined with ML algorithms. In our study, we concluded that using 2D:4D ratio and hand morphometry in estimating gender provides accurate and reliable data.

 Background: The present study was conducted to estimate gender from 2D:4D ratio and hand mor-phometry taken from participants by using machine learning ML algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women be-tween the ages of 18 and 30 who did not have any pathology, deformity or surgical interventions on their hands. Hand width HW, hand length HL, second digit length 2D, and fourth digit length 4D of the individuals were measured as the right R and left L side by using digital calliper and recorded in Excel. In addition, the ratio between the second digit and fourth digit 2D:4D of each individual was also recorded. Results: As a result of ML modelling, 0.92 accuracy was obtained with Random forest RF algorithm. With RF algorithm, all of the 16 women and 18 of the 21 men in the test set were estimated accu-rately. With SHAP analyzer of RF algorithm, HW-L parameter was found to have the highest contri-bution in estimating gender. The accuracy rates of the other ML models used in the study were found to vary between 0.78 and 0.89. Conclusions: It was found that 2D:4D ratio and hand morphometry measurements, which are fre-quently preferred in gender determination, have higher accuracy rate when examined with ML algorithms. In our study, we concluded that using 2D:4D ratio and hand morphometry in estimating gender provides accurate and reliable data.

 Amaç: Bu çalışma, makine öğrenimi ML algoritmaları kullanılarak katılımcılardan alınan 2D:4D oranından ve el morfometrisinden cinsiyet tahmini yapmak amacıyla gerçekleştirilmiştir. Materyal ve Metod: Çalışma, ellerinde herhangi bir patoloji, deformite veya cerrahi müdahale bulunmayan, yaşları 18-30 arasında değişen 88 erkek ve 96 kadın üzerinde retrospektif olarak ger-çekleştirildi. Bireylerin el genişliği HW, el uzunluğu HL, ikinci parmak uzunluğu 2D ve dördüncü parmak uzunluğu 4D dijital kumpas kullanılarak sağ R ve sol L taraf olarak ölçüldü ve kayıt altına alındı. Ayrıca her bireyin ikinci parmağı ile dördüncü parmağı arasındaki oran 2D:4D de kaydedildi. ML modellerinin girişinde elde edilen ölçümler kullanılarak cinsiyet tahmini yapılmıştır. Bulgular: ML modelleme sonucunda Random Forest RF algoritması ile 0,92 doğruluk elde edildi. RF algoritması ile test setindeki 16 kadın ve 21 erkekten 18inin tamamı doğru tahmin edilmiştir. RF algoritmasının SHAP analizörü ile cinsiyet tahmininde en yüksek katkıyı HW-L parametresinin sağla-dığı görülmüştür. Sonuç: Cinsiyet belirlemede sıklıkla tercih edilen 2D:4D oranı ve el morfometri ölçümlerinin ML algoritmaları ile incelendiğinde doğruluk oranının daha yüksek olduğu tespit edildi. Çalışmamızda cinsiyet tahmininde 2D:4D oranı ve el morfometrisinin kullanılmasının doğru ve güvenilir veri sağla-dığı sonucuna vardık.

Makale detayları

Gender Estimation from 2D:4D Ratio and Hand Morphometry by Using Machine Learning Algorithms

Yazarlar:

Ahmet Kurtoğlu | Rukiye Çiftçi

Yayıncı:

Harran University

Ana Başlıklar:

Spor Hekimliği | Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.21 Issue 2, pp.253-259

Belge Tipi:

Makale

Yayın Tarihi:

8/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323553

pISSN:

1304-9623

2D:4D ratio

hand morphometry

machine learning algorithms

gender estimation2D:4D oranı

el morfometrisi

makine öğrenme algoritmaları

cinsiyet tahmini

Sayı

Makale detayları

Yazarlar: Ahmet Kurtoğlu | Rukiye Çiftçi

Yayıncı: Harran University

Ana Başlıklar: Spor Hekimliği | Tıbbi Genetik (Kanser Genetiği hariç)

Dergi: Harran Üniversitesi Tıp Fakültesi Dergisi Vol.21 Issue 2, pp.253-259

Belge Tipi: Makale

Yayın Tarihi: 8/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001323553

pISSN: 1304-9623

Sayı

Yazarlar:

Ahmet Kurtoğlu | Rukiye Çiftçi

Yayıncı:

Harran University

Ana Başlıklar:

Spor Hekimliği | Tıbbi Genetik (Kanser Genetiği hariç)

Dergi:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.21 Issue 2, pp.253-259

Belge Tipi:

Makale

Yayın Tarihi:

8/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323553

pISSN:

1304-9623