Modeling severe ATV injuries using artificial neural networks

Abstract

This thesis develops a model for severe all-terrain vehicle (ATV) injuries using artificial neural networks (ANNs) with data from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) and analyzes the model to find the contribution of each factor in predicting severe injury. From the analysis of the model, recommendations are made on the factors that should be investigated further to reduce severe injuries. An analysis of ANN architecture shows that a configuration with no hidden nodes or layers results in optimal performance. The performance results of the ANN gives a logarithmic-sensitivity index of 0.09, sensitivity of 44%, specificity of 84%, correct classification rate (CCR) of 70% and receiver operating curve (ROC) area of 0.72. The most important input factors for predicting severe injury are: nature of injury, helmet, age group, mechanism, seat position, circumstances of collapse, body part and sex.