Adli bilim ve adli tıpta makine öğrenmesi: Literatür üzerine araştırma

Year 2022, Volume: 36 Issue: 1, 1 - 7, 01.04.2022

Sultan Turhan Mert Tunç Eralp Doğu , Yasemin Balcı

Abstract

Makine öğrenmesi yöntemleri, günümüzde dijitalleşme ile ortaya çıkan büyük veri setlerini anlamada ve yorumlamada oldukça başarılı sonuçlar elde etmektedir. Bu çalışmadaki amacımız, adli bilim ve adli tıp alanlarında makine öğrenmesi yöntemlerini araştırmak ve bu alandaki eğilimleri analiz etmektir.
Çalışmada PubMed veri tabanında 1988-2021 yılları arasında “Forensic Machine Learning” arama terimi kullanılarak 404 makale ve 1999-2021 yılları arasında “Forensic Medicine Machine Learning” arama terimi kullanılarak ortak olan 220 makaleye ulaşılmıştır.
Adli bilim ve adli tıp alanlarında makine öğrenme yöntemlerinin en sık cinsiyet ve yaş tahmininde kullanıldığı belirlenmiştir. Ayrıca en çok kullanılan makine öğrenmesi yönteminin “yapay sinir ağları” olduğu tespit edilirken en fazla kullanılan yöntem değerlendirme kriteri “doğruluk” olarak bulun-muştur.
Sonuç olarak; adli bilim ve adli tıpta yeni bir yaklaşım olan makine öğrenimine adli bilim ve adli tıp uzmanlarını makine öğrenimi çalışmalarına teşvik etmeyi amaçlıyoruz.

Keywords

Adli bilim, adli tıp, makine öğrenmesi, Pubmed

Machine learning in forensic science and forensic medicine: Research on the literature

Abstract

Machine learning methods achieve very successful results in understanding and interpreting the large data sets that have emerged with digitalization today. Our aim in this study is to investigate machine learning methods in the fields of forensic science and forensic medicine and to analyze the trends in this field.
In the study, 404 articles were reached by using the search term “Forensic Machine Learning” between 1988-2021 in PubMed database and 220 articles were shared by using the search term “Forensic Medicine Machine Learning” between 1999-2021.
It was determined that machine learning methods were used most frequently in the estimation of gender and age in the fields of forensic science and forensic medicine. In addition, while it was determined that the most used machine learning method was “artificial neural networks”, the most used method evaluation criterion was “accuracy”.
As a result, we aim to encourage forensic science and forensic medicine experts to work on machine learning, which is a new approach in forensic science and forensic medicine.

Keywords

Forensic science, forensics, machine learning, Pubmed

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