The application of decision trees for estimating osteological sex from common measurements of the skull

Skull measurements are commonly evaluated for osteological sex estimation in forensic anthropology, and decision tree-based classification models for the skull may improve accuracy compared to current metric methods. Additionally, decision trees can provide accurate sex classification with a limited number of measurements, which is valuable when analyzing fragmentary remains. Thus, the present study seeks to test the utility of decision trees for generating sex classification models from metric variables of the skull. Twenty-one skull measurements were evaluated for 403 adult males and females. Relative technical error of measurement was used to assess intraobserver error, and two-way ANOVAs and aligned rank transformation were used to examine the effects of sex, population, age, and temporal period on the measurements. The data set was split into 80% training and 20% holdout testing samples to assess the predictive accuracy of each tree. Trees were generated for the skull and cranium, with models for European Americans, African Americans, and the pooled population sample. Overall, the recommended trees for the cranium achieved higher accuracies (85.3-95.0%) compared to the skull trees (84.0-92.5%). Accuracies for the population-inclusive trees ranged from 84.0% to 85.3%, whereas the European American (92.5-95.0%) and African American (90.9%) trees achieved slightly higher accuracies. Improved accuracies were achieved compared to previous decision tree research as well as compared to current metric methods for the skull. These trees provide an additional option for estimating osteological sex, particularly when morphological methods do not yield adequate classification accuracies or cannot be assessed due to damage.