Citation

Erdebil Y, Frize M. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005; 1: 871-874.

Affiliation

Eng., Ottawa Univ., Ont.

Copyright

(Copyright © 2005, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2005.1616554

PMID

17282323

Abstract

This paper describes the development of a tool to predict the severity of all-terrain vehicle (ATV) injuries using artificial neural networks (ANNs). The data was obtained from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP). The main objective of the study was to identify the contribution of input variables in predicting severe injury or death. An ANN architecture with 9 hidden nodes and one hidden layer resulted in optimal performance: a logarithmic-sensitivity index of 0.099, sensitivity of 47.3%, specificity of 80.8%, correct classification rate (CCR) of 68.6% and receiver operating curve (ROC) area of 0.711. The minimum data set that can help predict injury severity is discussed.

Language: en