Age Classification in Forensic Medicine Using Machine Learning Techniques.

Autor:	Zolotenkova GV; Professor, Department of Forensic Medicine, First Moscow State Medical University (Sechenov University), 8/2 Malaya Trubetskaya St., Moscow, 119991, Russia; Researcher, Center for Information Technologies in Engineering of the Russian Academy of Sciences, 7а Marshala Biryuzova St., Moscow Region, Odintsovo, 143003, Russia., Rogachev AI; PhD Student, Big Data and Information Retrieval School, Faculty of Computer Science; HSE University, 11 Pokrovsky Boulevard, Moscow, 109028, Russia; Researcher, Center for Information Technologies in Engineering of the Russian Academy of Sciences, 7а Marshala Biryuzova St., Moscow Region, Odintsovo, 143003, Russia., Pigolkin YI; Professor, Corresponding Member of the Russian Academy of Sciences, Head of the Department of Forensic Medicine, First Moscow State Medical University (Sechenov University), 8/2 Malaya Trubetskaya St., Moscow, 119991, Russia; Researcher, Center for Information Technologies in Engineering of the Russian Academy of Sciences, 7а Marshala Biryuzova St., Moscow Region, Odintsovo, 143003, Russia., Edelev IS; Assistant, Department of Forensic Medicine; Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Square, Nizhny Novgorod, 603005, Russia., Borshchevskaya VN; Assistant, Department of Forensic Medicine; Stavropol State Medical University, 310 Mira St., Stavropol, 355017, Russia., Cameriere R; Professor, AgEstimation Project, Institute of Legal Medicine, University of Macerata, Macerata, 62100, Italy.
Jazyk:	angličtina
Zdroj:	Sovremennye tekhnologii v meditsine [Sovrem Tekhnologii Med] 2022; Vol. 14 (1), pp. 15-22. Date of Electronic Publication: 2022 Jan 28.
DOI:	10.17691/stm2022.14.1.02
Abstrakt:	The aim of the study was to assess the capabilities of age determination (age group) at death using classification techniques by histomorphometric characteristics of osseous and cartilaginous tissue aging. Materials and Methods: The study material was a database containing the findings of morphometric researches of osseous and cartilaginous tissue histologic specimens from 294 categorized male corpses aged 10-93 years. For data analysis and classification we used modern machine learning methods: k-NN, SVM, logistic regression, CatBoost, SGD, naive Bayes, random forest, nonlinear dimensionality reduction methods (t-SNE and uMAP), and recursive feature elimination for feature selection. Results: The used techniques (algorithms) provided effective representation of a complex data set (76 histomorphometric features), allowing to reveal the cluster structure inside the low dimensional feature space, thus fitting the classifier becomes even more reasonable. During feature selection, we estimated their importance for age group classification and studied the relationship between classification quality and the number of features inside the feature space. Data pre-processing made it possible to get rid of noise and keep most informative features, thereby accelerating a learning process and improving the classification quality. Data projection showed more well-defined cluster structure in the space of selected features. The accuracy of establishing certain groups was equal to 90%. It proves high efficiency of machine learning techniques used for forensic age diagnostics based on histomorphometric findings. Competing Interests: Conflicts of interest. The authors declare no conflicts of interest related to the present study.
Databáze:	MEDLINE
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