AgeEst: An open access web application for skeletal age-at-death estimation employing machine learning

Abstract

The present study tests the accuracy of commonly adopted age-at-death estimation markers based on the morphology of the pubic symphysis, iliac auricular surface and cranial sutures on a contemporary documented skeletal collection from Greece (81 males and 59 females). Machine learning techniques are used to assess whether a) machine learning classification models can correctly classify skeletons into their correct age group and b) machine learning regression models can predict the correct age to a satisfactory degree. The constructed models are used in a web application (AgeEst), where users can easily employ them to make predictions for their own skeletal assemblages. The results show that the use of machine learning improves age predictions in terms of bias and inaccuracy compared to the direct application of the original methods. However, there is a strong misclassification of middle-aged individuals, stressing the inherent biases both of the skeletal markers traditionally used in age-at-death prediction and of machine learning methods that, in our case, tend to classify most individuals to one of the two extremes (young or old). We would like to invite colleagues to share with us raw data from other skeletal collections to expand the training dataset to address to some extent issues of age mimicry, while the notebook used for the analysis as well as the code used to construct the web application are openly available to promote the further development of this or similar applications by other scholars.