Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114004
Title: | Blows or Falls? Distinction by Random Forest Classification | Authors: | Henriques, Mélanie Bonhomme, Vincent Cunha, Eugénia Adalian, Pascal |
Keywords: | forensic science; blunt force trauma; falls; blows; skeletal fractures; CT scan; random forests | Issue Date: | 29-Jan-2023 | Publisher: | MDPI | Serial title, monograph or event: | Biology | Volume: | 12 | Issue: | 2 | Abstract: | In this study, we propose a classification method between falls and blows using random forests. In total, 400 anonymized patients presenting with fractures from falls or blows aged between 20 and 49 years old were used. There were 549 types of fractures for 57 bones and 12 anatomical regions observed. We first tested various models according to the sensibility of random forest parameters and their effects on model accuracies. The best model was based on the binary coding of 12 anatomical regions or 28 bones with or without baseline (age and sex). Our method achieved the highest accuracy rate of 83% in the distinction between falls and blows. Our findings pave the way for applications to help forensic experts and archaeologists. | URI: | https://hdl.handle.net/10316/114004 | ISSN: | 2079-7737 | DOI: | 10.3390/biology12020206 | Rights: | openAccess |
Appears in Collections: | FCTUC Ciências da Vida - Artigos em Revistas Internacionais I&D CFE - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Blows-or-Falls-Distinction-by-Random-Forest-ClassificationBiology.pdf | 2.26 MB | Adobe PDF | View/Open |
Page view(s)
57
checked on Oct 9, 2024
Download(s)
8
checked on Oct 9, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License