|
|||||
|
|
Microscopic Saw Mark Analysis: An Empirical Approach |
|
| Authors: | Jennifer C. Love Ph.D. Sharon M. Derrick Ph.D. Jason M. Wiersema Ph.D. Charles Peters Ph.D. |
| Affiliation: | 1. Office of Chief Medical Examiner, 401 E St. SW, Washington, DC, 20024 Additional information and reprints requests: Jennifer C. Love, Ph.D., D-ABFA 401 E ST, SW Washington, DC 20024 E-mail: jennifer.love@dc.gov;2. Harris County Institute of Forensic Sciences, 1885 Old Spanish Trail, Houston, TX, 77054;3. Mathematics, The University of Houston, 4800 Calhoun Road, Houston, TX, 77004 |
| Abstract: | Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62–17.82%. |
| Keywords: | forensic science anthropology saw mark error rate classification tree random forest classifier |