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Age Classification in Forensic Medicine Using Machine Learning Techniques.

G V Zolotenkova¹, A I Rogachev², Y I Pigolkin³

¹Professor, 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.

Sovremennye Tekhnologii V Meditsine

|August 22, 2022

Summary

Machine learning accurately determines age at death using bone and cartilage tissue analysis. Histomorphometric features and advanced algorithms achieved 90% accuracy in forensic age diagnostics.

Keywords:

age diagnostics age groups forensic medicine machine learning techniques nonlinear dimensionality reduction methods

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Area of Science:

Forensic Science
Histology
Biometrics

Background:

Accurate age determination at death is crucial in forensic investigations.
Histomorphometric analysis of osseous and cartilaginous tissues offers potential for age estimation.

Purpose of the Study:

To evaluate the efficacy of machine learning classification techniques for age determination using histomorphometric features of bone and cartilage.
To assess the accuracy of age group classification based on these histological characteristics.

Main Methods:

Utilized a database of 294 male corpses (aged 10-93 years) with histologic specimens.
Applied machine learning algorithms including k-NN, SVM, logistic regression, CatBoost, SGD, naive Bayes, and random forest.
Employed nonlinear dimensionality reduction (t-SNE, uMAP) and recursive feature elimination for feature selection and data preprocessing.

Main Results:

Machine learning effectively represented complex histomorphometric data (76 features), revealing cluster structures in reduced dimensions.
Feature selection identified important features, improving classification quality and learning speed.
Achieved 90% accuracy in establishing specific age groups, demonstrating high efficiency.

Conclusions:

Machine learning techniques are highly effective for forensic age diagnostics.
Histomorphometric analysis of osseous and cartilaginous tissues, combined with machine learning, provides a reliable method for age determination at death.