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Constructing effective models for detecting, reducing, and/or preventing adverse events is very important in domains such as aviation safety, healthcare, drug administration, and war theaters. This study presents batch and data streaming models to detecting adverse events using data from a war theater context. In all the previous studies, regression models and several machine learning techniques were used for predicting continuous values in an active theater of war, and the error values reported on the test sets were large. In order to overcome the shortcoming, this study investigates the effectiveness of batch and data streaming classification algorithms in detecting or classifying adverse events given infrastructure development spending data and other variables in an active theater of war in Afghanistan. By the feature selection, the valid input variables are obtained and their indexes show that the input variables …