A comparison of statistical techniques in predicting violent outcomes in Thailand’s deep South
The classification of violent outcomes helps monitor their causes to prevent further loss among the public. The ability of statistical techniques to accurately predict the outcomes needs to be investigated. This study applied logistic regression (LR) and chi-squared automatic interaction detection decision tree (CHAID) techniques to predict physical and non-physical injuries which are considered violent outcomes. A set of 21,424 data about violent events from 2004 to 2016 were obtained from the Deep South Coordination Centre database and were divided into, and used as, training and testing datasets. Nine significant predictors, including arson, gun, bomb, time, province, day, quarter, zone, and district, were identified by LR as predicting violent outcomes. Likewise, only five factors, gun, zone, bomb, time, and arson, were represented in the CHAID results. However, the performances of LR and CHAID were not significantly different in terms of overall classification accuracy and area under the receiver operating characteristic curve.
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