Data mining for sex estimation based on cranial measurements

Toneva, Diana; Nikolova, Silviya; Agre, Gennady; Zlatareva, Dora; Hadjidekov, Vassil; Lazarov, Nikolai

doi:10.1016/j.forsciint.2020.110441

Cited by 16 publications

(17 citation statements)

References 76 publications

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“…The combined shape and size data have provided similar accuracy rates for both neuro- and viscerocranium: 92.2% and 92.9%, respectively. Furthermore, we have explored the cranial sexual dimorphism in the Bulgarian population using machine learning algorithms [ 41 , 43 ] and achieved an accuracy of 92% with a set of classification rules and more than 95% applying support vector machine and artificial neural network to cranial measurements. These results confirm that the sex differences in cranial size are well expressed in the studied population, but not in cranial shape.…”

Section: Discussionmentioning

confidence: 99%

“…In the traditional craniometric studies, bizygomatic breadth is one of the most dimorphic metric traits [ 7 , 14 , 15 , 17 , 21 , 22 , 23 , 24 , 34 , 35 , 38 , 70 ]. Moreover, we have developed classification rules and decision trees for sex estimation in an earlier study, where the bizygomatic breadth or non-standard measurements including the right/left zygion are present in all of the models for sex discrimination [ 43 ]. All this comes to show that the time of growth of a bone or bone structure is important for the development of sexually dimorphic traits.…”

Section: Discussionmentioning

confidence: 99%

“…Logistic regression analysis has also been commonly applied in sex estimation studies [ 22 , 23 , 24 , 37 , 38 ]. Currently, data mining and machine learning approaches including support vector machines [ 28 , 30 , 39 , 40 , 41 ], artificial neural networks [ 27 , 29 , 41 ], decision trees [ 42 , 43 ], and classification rule algorithms [ 43 ] are extensively applied.…”

Section: Introductionmentioning

confidence: 99%

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A Geometric Morphometric Study on Sexual Dimorphism in Viscerocranium

Toneva

Nikolova

Tasheva‐Terzieva

et al. 2022

Biology

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The level of sexual dimorphism manifested by human bones is an important factor for development of effective sex estimation methods. The aim of the study was to investigate the sexual dimorphism in the size and shape of the viscerocranium using geometric morphometric techniques. It also aimed to explore the sex differences in distinct viscerocranial regions and to establish the most dimorphic region with regard to size and shape. Computed tomography images of 156 males and 184 females were used in the study. Three-dimensional coordinates of 31 landmarks were acquired. Five landmark configurations were constructed from the viscerocranium and its orbital, nasal, maxillary, and zygomatic region. Generalized Procrustes superimposition, principal component analysis, and discriminant analysis were applied to each configuration. The significance of the sex differences in size and shape was assessed and significant differences were found in all configurations. The highest accuracy was obtained from both shape and size of the whole viscerocranium. Based on size only, the highest accuracy was achieved by the nasal region. The accuracy based on shape was generally low for all configurations, but the highest result was attained by the orbital region. Hence, size is a better sex discriminator than shape.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Geometric Morphometric Study on Sexual Dimorphism in Viscerocranium

Toneva

Nikolova

Tasheva‐Terzieva

et al. 2022

Biology

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“…While the morphological measurement is related to the observation of visual criteria [3]. Some parts of the body skeleton that are usually analyzed in determining sex are the pelvic [8][9][10][11], skull [12][13][14][15], mandible [16], cranial [17], femur [18][19][20][21], and tibia [22].…”

Section: Introductionmentioning

confidence: 99%

“…ML approaches are becoming a trend in determining the sex of skeletal remains, as research conducted by [2, 10,11,14,17,28,29]. ML is a branch of computer science that has the ability to learn and predict future outcomes with invisible data [11,28].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

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Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.

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