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Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning
dc.contributor.author | Gámez Granados, Juan Carlos | |
dc.contributor.author | Irurita Olivares, Javier | |
dc.contributor.author | Pérez Rodríguez, Francisco G.Raúl | |
dc.contributor.author | González Muñoz, Antonio | |
dc.contributor.author | Damas Arroyo, Sergio | |
dc.contributor.author | Alemán Aguilera, María Inmaculada | |
dc.contributor.author | Cordón García, Óscar | |
dc.date.accessioned | 2022-10-28T10:27:31Z | |
dc.date.available | 2022-10-28T10:27:31Z | |
dc.date.issued | 2022-09-05 | |
dc.identifier.citation | 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] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/77611 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | Ministry of Science and Innovation, Spain (MICINN) Spanish Government | es_ES |
dc.description.sponsorship | Agencia Estatal de Investigacion (AEI) PID2021-122916NB-I00 Spanish Government PGC2018-101216-B-I00 | es_ES |
dc.description.sponsorship | Junta de Andalucia | es_ES |
dc.description.sponsorship | University of Granada P18 -FR -4262 B-TIC-456-UGR20 | es_ES |
dc.description.sponsorship | European Commission | es_ES |
dc.description.sponsorship | Universidad de Granada/CBUA | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Forensic anthropology | es_ES |
dc.subject | Skeleton-based age assessment | es_ES |
dc.subject | Explainable artificial intelligence and machine learning | es_ES |
dc.subject | Ordinal classification | es_ES |
dc.subject | Oversampling methods | es_ES |
dc.title | Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1016/j.ins.2022.08.110 | |
dc.type.hasVersion | VoR | es_ES |