Gender Prediction Using Machine Learning Algorithms with Parameters Obtained from Calcaneus

İrem Nisa Korkmaz; Zülal Öner; Yusuf Seçgin; Serkan Öner

Article Details

Gender Prediction Using Machine Learning Algorithms with Parameters Obtained from Calcaneus

Authors:

İrem Nisa Korkmaz | Zülal Öner | Yusuf Seçgin | Serkan Öner

Publisher:

Harran University

Main Topics:

Adli Tıp | Klinik Tıp Bilimleri (Diğer)

Journal:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.22 Issue 2, pp.212-220

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001343946

pISSN:

1304-9623

Keywords:

Abstract:

 Background: Partial or total disruption of body integrity may occur in cases such as war, natural disasters and accidents. In such cases, the importance of the calcaneus bone, which has a hard and minimal structure, increases for identification. With this hypothesis, the aim of this study is to estimate gender from the calcaneus by utilising the current approach of machine learning ML algorithm. Materials and Methods: The study was performed on X-Ray images of 200 female and 200 male subjects aged 18-65 years. Maximum length, facies articularis cuboidea height, maximum width, body width, minimum length, anteroposterior length of the calcaneus, posterior facet angle, anterior angle of the cuboid facet of the calcaneus, facet height, posterior facet length, anterior proces length, calcaneus inclination angle, talocalcaneal angle, Böhler angle, Gissane angle and calcaneus tuber angle were measured. Then the obtained data were used in the input of ML algorithms. Results: As a result, a highly accurate and reliable sex prediction rate between 0.86-0.91 was obtained with ML algorithms. In addition, it was found that the maximum width of the calcaneus parameter made the highest contribution to sex prediction among the parameters with SHapley Additive exPlanations. Conclusions: As a result of our study, it was found that calcaneus with minimal and rigid structure provided high accuracy in terms of gender prediction using ML algorithms. In this respect, we think that this study will be a reference for forensic and morphometric studies.

 Background: Partial or total disruption of body integrity may occur in cases such as war, natural disasters and accidents. In such cases, the importance of the calcaneus bone, which has a hard and minimal structure, increases for identification. With this hypothesis, the aim of this study is to estimate gender from the calcaneus by utilising the current approach of machine learning ML algorithm. Materials and Methods: The study was performed on X-Ray images of 200 female and 200 male subjects aged 18-65 years. Maximum length, facies articularis cuboidea height, maximum width, body width, minimum length, anteroposterior length of the calcaneus, posterior facet angle, anterior angle of the cuboid facet of the calcaneus, facet height, posterior facet length, anterior proces length, calcaneus inclination angle, talocalcaneal angle, Böhler angle, Gissane angle and calcaneus tuber angle were measured. Then the obtained data were used in the input of ML algorithms. Results: As a result, a highly accurate and reliable sex prediction rate between 0.86-0.91 was obtained with ML algorithms. In addition, it was found that the maximum width of the calcaneus parameter made the highest contribution to sex prediction among the parameters with SHapley Additive exPlanations. Conclusions: As a result of our study, it was found that calcaneus with minimal and rigid structure provided high accuracy in terms of gender prediction using ML algorithms. In this respect, we think that this study will be a reference for forensic and morphometric studies.

 Background: Partial or total disruption of body integrity may occur in cases such as war, natural disasters and accidents. In such cases, the importance of the calcaneus bone, which has a hard and minimal structure, increases for identification. With this hypothesis, the aim of this study is to estimate gender from the calcaneus by utilising the current approach of machine learning ML algorithm. Materials and Methods: The study was performed on X-Ray images of 200 female and 200 male subjects aged 18-65 years. Maximum length, facies articularis cuboidea height, maximum width, body width, minimum length, anteroposterior length of the calcaneus, posterior facet angle, anterior angle of the cuboid facet of the calcaneus, facet height, posterior facet length, anterior proces length, calcaneus inclination angle, talocalcaneal angle, Böhler angle, Gissane angle and calcaneus tuber angle were measured. Then the obtained data were used in the input of ML algorithms. Results: As a result, a highly accurate and reliable sex prediction rate between 0.86-0.91 was obtained with ML algorithms. In addition, it was found that the maximum width of the calcaneus parameter made the highest contribution to sex prediction among the parameters with SHapley Additive exPlanations. Conclusions: As a result of our study, it was found that calcaneus with minimal and rigid structure provided high accuracy in terms of gender prediction using ML algorithms. In this respect, we think that this study will be a reference for forensic and morphometric studies.

 Amaç: Savaş, doğal afetler ve kazalar gibi durumlarda vücut bütünlüğünün kısmen veya tamamen bozulması söz konusu olabilir. Bu gibi durumlarda sert ve minimal bir yapıya sahip olan kalkaneus kemiğinin kimlik tespiti için önemi artmaktadır. Bu hipotezle, bu çalışmanın amacı makine öğrenmesi ML algoritmasının güncel yaklaşımını kullanarak calcaneustan cinsiyet tahmini yapmaktır. Materyal ve Metot: Çalışma 18-65 yaş arası 200 kadın ve 200 erkek bireyin X-Ray görüntüleri üzerinde gerçekleştirildi. Maksimum uzunluk, facies articularis cuboidea yüksekliği, maksimum genişlik, gövde genişliği, minimum uzunluk, calcaneusun ön-arka uzunluğu, arka faset açısı, calcaneusun küboid fasetinin ön açısı, faset yüksekliği, arka faset uzunluğu, ön proces uzunluğu, calcaneus inklinasyon açısı, talokalkaneal açı, Böhler açısı, Gissane açısı ve kalkaneus tuber açısı ölçüldü. Daha sonra elde edilen veriler ML algoritmalarının girişinde kullanıldı. Bulgular: Sonuç olarak, ML algoritmaları ile 0,86-0,91 arasında oldukça doğru ve güvenilir bir cinsiyet tahmin oranı elde edilmiştir. Ayrıca SHapley Additive exPlanations ile parametreler arasında cinsiyet tahminine en yüksek katkıyı calcaneusun maksimum genişliği parametresinin yaptığı tespit edildi. Sonuçlar: Çalışmamız sonucunda minimal ve rijit yapıya sahip calcaneusun ML algoritmaları kullanılarak cinsiyet tahmini açısından yüksek doğruluk sağladığı tespit edilmiştir. Bu açıdan bu çalışmanın adli ve morfometrik çalışmalar için referans olacağını düşünmekteyiz.

Article Details

Gender Prediction Using Machine Learning Algorithms with Parameters Obtained from Calcaneus

Authors:

İrem Nisa Korkmaz | Zülal Öner | Yusuf Seçgin | Serkan Öner

Publisher:

Harran University

Main Topics:

Adli Tıp | Klinik Tıp Bilimleri (Diğer)

Journal:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.22 Issue 2, pp.212-220

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001343946

pISSN:

1304-9623

Calcaneus

X-Ray

machine learning algorithms

gender predictionCalcaneus

X-Ray

makine öğrenmesi algoritmaları

cinsiyet tahmini

Issues

Article Details

Authors: İrem Nisa Korkmaz | Zülal Öner | Yusuf Seçgin | Serkan Öner

Publisher: Harran University

Main Topics: Adli Tıp | Klinik Tıp Bilimleri (Diğer)

Journal: Harran Üniversitesi Tıp Fakültesi Dergisi Vol.22 Issue 2, pp.212-220

Document Type: Article

Publication Date: 6/2025

Document Language: English

Document ID (Org No.): 2001343946

pISSN: 1304-9623

Issues

Authors:

İrem Nisa Korkmaz | Zülal Öner | Yusuf Seçgin | Serkan Öner

Publisher:

Harran University

Main Topics:

Adli Tıp | Klinik Tıp Bilimleri (Diğer)

Journal:

Harran Üniversitesi Tıp Fakültesi Dergisi Vol.22 Issue 2, pp.212-220

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001343946

pISSN:

1304-9623