@article{ref1,
title="An Analysis Of CHIRPP Data To Predict Severe ATV Injuries Using Artificial Neural Networks",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2005",
author="Erdebil, Y. and Frize, M.",
volume="1",
number="",
pages="871-874",
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.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/IEMBS.2005.1616554",
url="http://dx.doi.org/10.1109/IEMBS.2005.1616554"
}