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

Background: The present study was conducted to estimate gender from 2D:4D ratio and hand morphometry taken from participants by using machine learning (ML) algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women between the ages of 18 and 30 who did not have...

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Publicado en:Journal of Harran University Medical Faculty / Harran Üniversitesi Tıp Fakültesi Dergisi Vol. 21; no. 2; pp. 253 - 260
Autores principales: KURTOGLU, Ahmet, CIFTCI, Rukiye
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: Harran University Medical Faculty 2024
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Journal of Harran University Medical Faculty / Harran Üniversitesi Tıp Fakültesi Dergisi
      issn: 13049623
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      dt: 2024
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      pub: Harran University Medical Faculty
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        10.35440/hutfd.1475317
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        atl: Gender Estimation from 2D:4D Ratio and Hand Morphometry by Using Machine Learning Algorithms.
      aug:
        au:
          KURTOGLU, Ahmet
          CIFTCI, Rukiye
        affil: Bandirma Onyedi Eylul University, Faculty of Sports Sciences, Department of Coaching, Bandırma, Balıkesir,TURKIYE
      sug:
        subj:
          Machine Learning Algorithms
          Sex Factors
          Hand Anatomy and Histology
          Fingers Anatomy and Histology
          Human
          Retrospective Design
          Male
          Female
          Adult
          Random Forest
          Adult: 19-44 years
          Male
          Female
      ab:
        Background: The present study was conducted to estimate gender from 2D:4D ratio and hand morphometry taken from participants by using machine learning (ML) algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women between 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 accurately. With SHAP analyzer of RF algorithm, HW-L parameter was found to have the highest contribution 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 frequently 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 gerçekleştirildi. 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 18'inin tamamı doğru tahmin edilmiştir. RF algoritmasının SHAP analizörü ile cinsiyet tahmininde en yüksek katkıyı HW-L parametresinin sağladığı 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 sağladığı sonucuna vardık.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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