Accurate prediction of saw blade thicknesses from false start measurements. (January 2021)

• Record Type: Journal Article
• Authors: Alsop, K.; Baier, W.; Norman, D.; Burnett, B.; Williams, M.A.
• Is Part Of: Forensic science international. Volume 318(2021)
• Publication Date: 2021-01
• Subjects: False starts -- Saw Marks -- Forensic -- Toolmarks -- Statistical models -- Random forest -- Accurate prediction; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2020.110602
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: Increased objectivity is necessary for pattern-based forensic sciences. False start profile measurements are useful for statistical analysis. Random forest prediction of saw blade thickness is applicable to false starts. Models can predict saw blade thickness accurately up to 100%. 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.