Sex Estimation from the First and Second Ribs Using 3D Postmortem CT Images in a Japanese Population: A Comparison of Discriminant Analysis and Machine Learning Techniques

Abstract

This study investigated the use of 3D postmortem computed tomography (PMCT) images of the first and second ribs for sex estimation in a Japanese population. Sex estimation models using conventional discriminant analysis and ten machine learning algorithms including logistic regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and extra tree (ET), were achieved from PMCT measurements of the first and second rib and the accuracy of models were compared. The results showed that ML algorithms, particularly LR, outperformed discriminant analysis, achieving an accuracy of 83.6% compared to 79.1% for stepwise discriminant analysis. This study highlights the potential of 3D PMCT and ML for accurate sex estimation in forensic anthropology.