Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers.

The assessment of sex is of paramount importance in the establishment of the biological profile of a skeletal individual. Femoral relevance for sex estimation is indisputable, particularly when other exceedingly dimorphic skeletal regions are missing. As such, this study intended to generate population-specific osteometric models for the estimation of sex with the femur and to compare the accuracy of the models obtained through classical and machine-learning classifiers. A set of 15 standard femoral measurements was acquired in a training sample (100 females; 100 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal) and models for sex classification were produced with logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM), and reduce error pruning trees (REPTree). Under cross-validation, univariable sectioning points generated with REPTree correctly estimated sex in 60.0-87.5% of cases (systematic error ranging from 0.0 to 37.0%), while multivariable models correctly classified sex in 84.0-92.5% of cases (bias from 0.0 to 7.0%). All models were assessed in a holdout sample (24 females; 34 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with an allocation accuracy ranging from 56.9 to 86.2% (bias from 4.4 to 67.0%) in the univariable models, and from 84.5 to 89.7% (bias from 3.7 to 23.3%) in the multivariable models. This study makes available a detailed description of sexual dimorphism in femoral linear dimensions in two Portuguese identified skeletal samples, emphasizing the relevance of the femur for the estimation of sex in skeletal remains in diverse conditions of completeness and preservation.