The assessment of age-at-death is an important and challenging part of investigations of human skeletal remains. The main objective of the present study was to apply different mathematical approaches in order to reach more accurate and reliable results in age estimation. A multi-ethnic dataset (n=941) of evaluated age-related changes on the pubic symphysis and the auricular surface of the hip bone was used. Two research groups examined nine different mathematical approaches. The best results were reached by Multi-linear regression, followed by the Collapsed regression model, with MAE values of 9.7 and 9.9 years, respectively, and with RMSE values of 12.1 and 12.2, respectively. The mean accuracy of decision tree models ranged between 30.7% and 72.3%, with the model using only the PUSx indicator performing the best. Moreover, our results indicate that the limiting factor of age estimation can be the visual evaluation of age-related changes. Further research is required to objectify the proposed methods for estimating age.

Centre for Functional Ecology Laboratory of Forensic Anthropology Department of Life Sciences University of Coimbra Calçada Martim de Freitas 3000 456 Coimbra Portugal

Department of Anthropology and Human Genetics Faculty of Science Charles University Viničná 7 Prague 128 43 Czech Republic

Faculty of Information Technology Technical University Prague Thákurova 9 Prague 160 00 Czech Republic

References provided by Crossref.org

The computational age-at-death estimation from 3D surface models of the adult pubic symphysis using data mining methods

A validation study of the Stoyanova et al. method (2017) for age-at-death estimation quantifying the 3D pubic symphyseal surface of adult males of European populations