Risk factors quantification based on mutual information ratio for in depth investigation of real world accidents database

In Europe traffic accidents are now widely recorded in national data bases. In view of the massive amounts of accident data, the use of data mining tools is essential to sift truly relevant information, and to extract reliable relations between injuries severity and potential causation factors. We present an innovative data mining approach for in depth investigation of causation in accidents data bases. Classical statistical tools evaluate the strength of potential causal relationships by essentially linear techniques, or strongly rely on ad hoc specific models. We outline here how mutual information ratios (based on conditional entropies) contribute to rigorously quantify the influence of causation factors on accident outcome descriptors such as injury type and severity. Information theoretic methods