Propuesta supervisada de aprendizaje de máquina para la estimación de la edad biológica humana basada en odontología forense

Johana Gabriela Becerra Alvarez; Jimy Alexander Cortés Osorio

doi:10.24050/reia.v17i34.1392

Supervised Machine Learning Proposal For The Estimation Of Human Biological Age Based On Forensic Dentistry

10.24050/reia.v17i34.1392

Published 2020-11-09

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Supervised Machine Learning Proposal For The Estimation Of Human Biological Age Based On Forensic Dentistry (J. G. Becerra Alvarez & J. A. Cortés Osorio, Trans.). (2020). Revista EIA, 17(34), 1-19. https://doi.org/10.24050/reia.v17i34.1392

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Abstract

Colombian Criminal Procedure Code supports the appropriate use of dental pieces for the identification of individuals, so this technique is a tool of great importance for the administration of justice institutions. The classic methods of human identification, especially those used to estimate the biological age of death in adults such as Lamendin, have been developed using information from populations with morphological, metric and cultural characteristics different from those in Colombia. Consequently, Colombian forensic specialists have obtained high error rates in age estimation results. In this research, the biological age was initially calculated using the classic method of Lamendin. Subsequently, with the direct and indirect measures, also used in the Lamendin method, the training of machine learning techniques was carried out knowing the chronological age of the individuals in the sample. The results of the supervised machine learning regression techniques were compared: Vector Support Machines (SVM), Gaussian Regression Processes (GPR) and Ensembles of Trees (EoT), among others. A sample of 48 single-root dental pieces belonging to 45 individuals of Colombian nationality was studied, with chronological ages of death known within the age range from 19 to 81 years.

Additionally, the digital caliper gauge and the comparison microscope were used to measure the heights of the morphological characteristics in the dental pieces. The best accuracy in the estimation of the biological age was achieved with the GPR regression that showed a root mean square error (RMSE) of 3.37 years in the identification, which was compared with the accuracy reached by the Lamendin method with a RMSE of 15.52 years. This research shows that the GPR machine learning regression, especially for the Colombian case, is a valid tool for estimating the biological age of death with much lower errors than those achieved by traditional European techniques.

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Authors

Johana Gabriela Becerra Alvarez

Universidad Tecnológica de Pereira

http://orcid.org/0000-0002-9099-6504 (unauthenticated)
Jimy Alexander Cortés Osorio

Universidad Tecnológica de Pereira

http://orcid.org/0000-0002-0413-807X (unauthenticated)

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Supervised Machine Learning Proposal For The Estimation Of Human Biological Age Based On Forensic Dentistry