The forensic use of percutaneous femur length in height and sex estimation among Ghanaians

Tetteh, Joshua; Appiah, Atta Kusi; Abaidoo, Chrissie Stansie; Adjei-Antwi, Collins

doi:10.1016/j.fsir.2021.100234

Cited by 1 publication

(3 citation statements)

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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%

“…The pelvic bones are known as a reliable and best part of the skeleton to reach a diagnosis in sex determination [7,18]. The pelvic bones consist of a pair of hip bones, the coccyx, and the sacrum.…”

Section: Introductionmentioning

confidence: 99%

“…In classification, the training data is The application of classification techniques has been widely applied in forensic anthropology, particularly in the case of sex determination. In previous studies, the most popular classification techniques in determining sex were Discriminant Function Analysis (DFA) which was studied by [10,15,22,24,25] and Logistic Regression (LR) by [18,[25][26][27]. In contrast, over the last few years there has been a trend in forensic anthropology to adopt and apply machine learning (ML) approaches [11,23].…”

Section: Introductionmentioning

confidence: 99%

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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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