Title :
An Analysis Of Chirpp Data To Predict Severe ATV Injuries Using Artificial Neural Networks
Author :
Erdebil, Y. ; Frize, M.
Author_Institution :
Sch. of Information Technol. & Eng., Ottawa Univ., Ont.
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
Keywords :
medical computing; neural nets; sensitivity analysis; CHIRPP data; Canadian Hospitals Injury Reporting and Prevention Program; all-terrain vehicle; artificial neural networks; correct classification rate; logarithmic-sensitivity index; receiver operating curve; severe ATV injuries; Artificial neural networks; Chirp; Data analysis; Drugs; Engineering in medicine and biology; HDTV; Hospitals; Injuries; Predictive models; Testing; ATVs; Artificial Neural Networks; injury; minimum data set;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616554
