Şenol, Gamze TaşkınKürtül, İbrahimRay, AbdullahRay, Gülçin2024-09-252024-09-2520240717-95020717-9367https://hdl.handle.net/20.500.12491/14015Since machine learning algorithms give more reliable results, they have been used in the field of health in recent years. The orbital variables give very successful results in classifying sex correctly. This research has focused on sex determinationusing certain variables obtained from the orbital images of the computerized tomography (CT) by using machine learning algorithms (ML).is In study th 12 variables determined on 600 orbital images of 300 individuals (150 men and 150 women) were tested with different ML. ree Decision (DT), t algorithms of ML were used for unsupervised learning. Statistical analyses of the variables were conducted with Minitab (R) 64-bit)21.2 ( program. ACC rate of NB, DT, KNN, and LR algorithms was found as % 83 while the ACC rate of LDA and RFC algorithms was determind as % 85. According to Shap analysis, the variable with the highest degree of effect was found as BOW. The study has thedetermined sex with high accuracy at the ratios of 0.83 and 0.85 through using the variables of the orbital CT images, and the related morphometricdata of the population under question was acquired, emphasizing the racial variation.eninfo:eu-repo/semantics/closedAccessSex DeterminationMachine LearningOrbital ApertureThree-Dimensional Computed TomographyEmphasizing the Racial VariationLR AlgorithmsArticle4249709762-s2.0-85203080201Q3WOS:001308286200013N/A