Machine Learning Based Approaches for Sex Identification in Bioarchaeology

In this paper we approach from a machine learningperspective the problem of identifying the sex of archaeologicalremains from anthropometric data, an important problem withinthe field of bioarchaeology. As the conditions for detecting thesex of a skeleton are not entirely known, machine learning baseddata mining models are appropriate to address this problem sincethey are able to capture unobservable patterns in data. Thesepatterns could be relevant for classifying a skeletal remain asmale or female. We propose two machine learning models basedon artificial neural networks for identifying the sex of humanskeletons from bone measurements. The proposed models areexperimentally evaluated on case studies generated from twodata sets publicly available in the archaeological literature. Theobtained results show that the proposed data mining modelsare effective for detecting the sex of archaeological remains, confirming the potential of our proposal.