Sex Estimation With Ensemble Learning: an Analysis Using Anthropometric Measurements of Piriform Aperture

Abstract

BackgroundPiriform aperture is an anatomical region that has been very little studied in terms of sex estimation. Ensemble learning is similarly an unstudied area in sex estimation from human skeletal remains. In this study, it was aimed to perform sex estimation by using the anthropometric measurements of piriform aperture obtained by computed tomography and 3D reconstruction techniques, discriminant function analysis, machine learning algorithms, and ensemble learning method. A total of 442 cases, 226 male and 216 female, aged between 21 and 89 were included in the study. After sex estimation was performed using discriminant analysis, K-nearest neighbor, Gaussian Naive Bayes, multilayer perceptron neural networks, decision trees, support vector machines, and random forest algorithms, a random forest model that accepted the results of these seven methods as predictors was created, and sex estimation was performed again with ensemble learning.ResultsSex prediction results were obtained with a maximum accuracy of 76.5% with discriminant analysis, 84.2% with machine learning algorithms, and 85.7% with the ensemble learning method.ConclusionsIn conclusion, it was seen that piriform aperture showed moderate sexual dimorphism. Sex estimation results could be further improved with machine learning algorithms and especially with the ensemble learning method.