Gender specific classification of road accident patterns through data mining techniques

Road accident analysis is very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. In this research work we applied data mining classification techniques to carry out gender based classification of which RndTree and C4.5 using AdaBoost Meta classifier gives high accurate results. The training dataset used for the research work is obtained from Fatality Analysis Reporting System (FARS) which is provided by the University of Alabama's Critical Analysis Reporting Environment (CARE) system. The results reveal that AdaBoost used with RndTree improvised the classifier's accuracy.