Revisiting metric sex estimation of burnt human remains via supervised learning using a reference collection of modern identified cremated individuals (Knoxville, USA).

OBJECTIVES

This study aims to increase the rate of correctly sexed calcined individuals from archaeological and forensic contexts. This is achieved by evaluating sexual dimorphism of commonly used and new skeletal elements via uni- and multi-variate metric trait analyses.

MATERIALS AND METHODS

Twenty-two skeletal traits were evaluated in 86 individuals from the William M. Bass donated cremated collection of known sex and age-at-death. Four different predictive models, logistic regression, random forest, neural network, and calculation of population specific cut-off points, were used to determine the classification accuracy (CA) of each feature and several combinations thereof.

RESULTS

An overall CA of ≥ 80% was obtained for 12 out of 22 features (humerus trochlea max., and lunate length, humerus head vertical diameter, humerus head transverse diameter, radius head max., femur head vertical diameter, patella width, patella thickness, and talus trochlea length) using univariate analysis. Multivariate analysis showed an increase of CA (≥ 95%) for certain combinations and models (e.g., humerus trochlea max. and patella thickness). Our study shows metric sexual dimorphism to be well preserved in calcined human remains, despite the changes that occur during burning.

CONCLUSIONS

Our study demonstrated the potential of machine learning approaches, such as neural networks, for multivariate analyses. Using these statistical methods improves the rate of correct sex estimations in calcined human remains and can be applied to highly fragmented unburnt individuals from both archaeological and forensic contexts.