Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning
Metadatos
Afficher la notice complèteAuteur
Gámez Granados, Juan Carlos; Irurita Olivares, Javier; Pérez Rodríguez, Francisco G.Raúl; González Muñoz, Antonio; Damas Arroyo, Sergio; Alemán Aguilera, María Inmaculada; Cordón García, ÓscarEditorial
Elsevier
Materia
Forensic anthropology Skeleton-based age assessment Explainable artificial intelligence and machine learning Ordinal classification Oversampling methods
Date
2022-09-05Referencia bibliográfica
Juan Carlos Gámez-Granados... [et al.]. Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning, Information Sciences, Volume 612, 2022, Pages 514-535, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2022.08.110]
Patrocinador
Ministry of Science and Innovation, Spain (MICINN) Spanish Government; Agencia Estatal de Investigacion (AEI) PID2021-122916NB-I00 Spanish Government PGC2018-101216-B-I00; Junta de Andalucia; University of Granada P18 -FR -4262 B-TIC-456-UGR20; European Commission; Universidad de Granada/CBUARésumé
Age estimation is a fundamental task in forensic anthropology for both the living and the
dead. The procedure consists of analyzing properties such as appearance, ossification patterns,
and morphology in different skeletonized remains. The pubic symphysis is extensively
used to assess adults’ age-at-death due to its reliability. Nevertheless, most
methods currently used for skeleton-based age estimation are carried out manually, even
though their automation has the potential to lead to a considerable improvement in terms
of economic resources, effectiveness, and execution time. In particular, explainable
machine learning emerges as a promising means of addressing this challenge by engaging
forensic experts to refine and audit the extracted knowledge and discover unknown patterns
hidden in the complex and uncertain available data. In this contribution we address
the automation of the decision making process of Todd’s pioneering age assessment
method to assist the forensic practitioner in its application. To do so, we make use of the
pubic bone data base available at the Physical Anthropology lab of the University of
Granada. The machine learning task is significantly complex as it becomes an imbalanced
ordinal classification problem with a small sample size and a high dimension. We tackle it
with the combination of an ordinal classification method and oversampling techniques
through an extensive experimental setup. Two forensic anthropologists refine and validate
the derived rule base according to their own expertise and the knowledge available in the
area. The resulting automatic system, finally composed of 34 interpretable rules, outperforms
the state-of-the-art accuracy. In addition, and more importantly, it allows the forensic
experts to uncover novel and interesting insights about how Todd’s method works, in
particular, and the guidelines to estimate age-at-death from pubic symphysis characteristics,
generally.