Year: 2026
Type: article
Abstract: Context—The skull is a crucial reference structure in skeletal analysis due to its hard and durable nature, which enables it to resist environmental degradation and preserve its morphological characteristics over extended periods. Because of these properties, craniofacial structures are widely used in forensic science, anthropology, and medical studies for identification purposes, including sex determination.Objective—This study aims to perform sex determination using cephalometric radiography (CR) and to evaluate the effectiveness of statistical and correlation-based feature selection methods in improving classification performance. Additionally, the study investigates whether a reduced set of informative cephalometric features (CFs) can achieve comparable accuracy to the full feature set, thereby increasing model efficiency and interpretability.Method—A dataset was created by extracting the and coordinates of 19 cephalometric landmarks (CLs) from CR images of 300 patients (150 females and 150 males). Based on these landmarks, 8 Euclidean distance (8-ED), 3 angular (3-A), and 3 frontal sinus (3-FS) measurements were calculated, resulting in a total of 14 CFs. Initially, statistical analyses were conducted to determine the relationship between each feature and the sex variable, identifying the most discriminative parameters. Following this, correlation analysis was performed to examine inter-feature relationships and to detect redundant information. Feature combinations with high correlation and strong association with the target variable were generated using predefined threshold values. To evaluate the effectiveness of these feature sets, comparative classification experiments were carried out using machine learning (ML) algorithms, with a particular focus on performance differences between full and reduced feature subsets.Results—The experimental results showed that using the Support Vector Machine (SVM) classifier with 4 CFs yielded an accuracy of 89.33%. Notably, a comparable level of classification performance was achieved using only 4 CFs selected based on strong statistical significance and high correlation with the sex class. This finding suggests that many of the original CFs contain redundant or less informative data, and that careful feature selection can simplify the model without compromising its predictive capability.Conclusion—The results demonstrate that effective feature selection based on statistical and correlation analyses can significantly reduce the number of CFs required while maintaining high classification performance in sex determination from cephalometric radiographs. This reduction not only improves computational efficiency but also enhances model interpretability and applicability in real-world scenarios. The proposed approach provides a robust framework for optimizing feature sets in similar biomedical classification problems.
Authors: Gizemnur Erol Doğan, Betül Uzbaş, Hatice Kök
Institutions: Konya Technical University, Selçuk University
Language: Turkish
Subfield: Archeology
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7201969306