Alsop, K., Baier, Waltraud, Norman, D. G., Burnett, B. and Williams, M. A. (2021) Accurate prediction of saw blade thicknesses from false start measurements. Forensic Science International, 318 . 110602. doi:10.1016/j.forsciint.2020.110602

Preview	PDF WRAP-accurate-prediction-saw blade-thicknesses-false-start-measurements-Williams-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (797Kb) | Preview
	PDF WRAP-accurate-prediction-saw blade-thicknesses-false-start-measurements-Williams-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2663Kb)

Request Changes to record.

Abstract

Background:
False start analysis is the examination of incomplete saw marks created on bone in an effort to establish information on the saw that created them. The present study aims to use quantitative data from micro-CT cross-sections to predict the thickness of the saw blade used to create the mark. Random forest statistical models are utilised for prediction to present a methodology that is useful to both forensic researchers and practitioners.

Method:
340 false starts were created on 32 fleshed cadaveric leg bones by 38 saws of various classes. False starts were micro-CT scanned and seven measurements taken digitally. A regression random forest model was produced from the measurement data of all saws to predict the saw blade thickness from false starts with an unknown class. A further model was created, consisting of three random forests, to predict the saw blade thickness when the class of the saw is known. The predictive capability of the models was tested using a second sample of data, consisting of measurements taken from a further 17 false starts created randomly selected saws from the 38 in the experiment.

Results:
Random forest models were able to accurately predict up to 100% of saw blade thicknesses for both samples of false starts.

Conclusion:
This study demonstrates the applicability of random forest statistical regression models for reliable prediction of saw blade thicknesses from false start data. The methodology proposed enables prediction of saw blade thickness from empirical data and offers a significant step towards reduced subjectivity and database formation in false start analysis. Application of this methodology to false start analysis, with a more complete database, will allow complementary results to current analysis techniques to provide more information on the saw used in dismemberment casework.

Item Type:	Journal Article
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Forensic sciences -- Statistical methods, Tools -- Identification, Saws -- Identification -- Statistical methods, Microcomputed tomography
Journal or Publication Title:	Forensic Science International
Publisher:	Elsevier Science BV
ISSN:	0379-0738
Official Date:	January 2021
Dates:	Date Event January 2021 Published 18 November 2020 Available 11 November 2020 Accepted
Date of first compliant deposit:	2 December 2020
Volume:	318
Article Number:	110602
DOI:	10.1016/j.forsciint.2020.110602
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access

Request changes or add full text files to a record

Repository staff actions (login required)

View Item