Sex estimation from the variables of talocrural joint by using machine learning algorithms

Abstract

This study has focused on sex determination from the variables estimated on X-ray images of the talocrural joint by using machine learning algorithms (ML). The variables of the mediolateral diameter of tibia (TMLD) and fibula (FMLD), the distance between the innermost points of the talocrural joint (DIT), the distance between the outermost points of the talocrural joint (DOT), and the distal articular surface of the tibia (TAS) estimated using X-ray images of 150 women and 150 men were evaluated by applying different ML methods. Logistic regression classifier, Decision Tree classifier, K-Nearest Neighbor classifier, Linear Discriminant Analysis, Naive Bayes and Random Forest classifier were used as algorithms. As a result of ML, an accuracy between 82 and 92 % was found. The highest rate of accuracy was achieved with RFC algorithm. DOT was the variable which contributed to the model at highest degree. Except for the variables of the age and FMLD, the other variables were found to be statistically significant in terms of sex difference. It was found that the variables of the talocrural joint were classified with high accuracy in terms of sex. In addition, morphometric data were found about the population and racial differences were emphasized.