When considering the impermanent environment of construction projects for electrical contractors, it is not always easy to discover the root cause of an accident. In addition, although previous studies have indicated that most accidents can be attributed to spatial and temporal interactions of multiple features, a limited number of studies have empirically explored these relationships. One data-mining technique that can be used to address these limitations and investigate the causal relationship among events is a classification and regression tree (CART). The current study applied CART analysis to explore the relationship between major characteristics of a construction project and/or work attributes, and injury outcomes. CART is an appropriate statistical analytical tool for analyzing data with non-linear relationships, a high-order of interactions, and a large number of missing values. To analyze a reliable and representative database of accidents involving electricians, content analysis was conducted on 320 accident reports obtained from the Occupational Safety and Health Administration database to identify attributes that led to accidents. All accidents occurred between 2009 and 2012. The outcomes of accidents (fatality versus no-fatality) were considered as dependent variables, and features such as project cost, project type, project end-use, and task attributes that cause accidents were considered as independent variables. The findings describe how specific characteristics of a project (e.g. cost) or a construction task (e.g. working near wiring) can be used to predict the probability of fatality for an electrician on a jobsite. The results of the study also can be used by safety managers to revise safety practices and training programs.