Mining human failure dynamics from accident data using logistic regression and decision trees

The effective operation of technology depends on the decision-making of the humans operating that technology. Of fundamental interest are the conditions that may lead to failure or accidents. The research to understand human decision-making processes that lead to failure under varying conditions has typically approached the problem either deductively or inductively through surveys or small-scale experiments. This paper describes an inductive approach based on mining multiple accident data sets for relationships between environmental factors, human factors, operational stimuli, and the probability of correct response by the human operators. We describe data mining techniques we have developed for this problem and then show their applicability to train accident data.