Predictive Models for Classifying the Outcomes of Violence Case Study for Thailand's Deep South

Bunjira Makond and Mayuening Eso
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Bunjira Makond: Faculty of Commerce and Management, Prince of Songkla University, Trang, Thailand
Mayuening Eso: Faculty of Science and Technology, Prince of Songkla University, Pattani, Thailand

Advances in Decision Sciences, 2019, vol. 23, issue 3, 56-92

Abstract: Violence is now widely recognized as a public health problem because of its significant consequences on the health and wellness of people and it remains a growing problem in many countries including Thailand. Elucidating the factors related to violence can provide information that can help to prevent violence and decrease the number of injuries. This study explored predictive data mining models which have high interpretability and prediction accuracy in classifying the outcomes of violence. After data preprocessing, a set of 21,424 incidents occurring from 2004 to 2016 were obtained from the Deep South Coordination Centre database. A correlation-based feature subset selection and decision tree technique with embedded feature selection were used for variable selection and four data mining techniques were applied to classify the violent outcomes into physical injury and no physical injury. The findings revealed that regardless of the variable selection method, gun was selected as a risk factor of physical injury. Moreover, a decision tree model with three variables, gun, zone, and solid/sharp weapon outperformed a naive Bayes model in terms of accurate performance and interpretability. Decision tree and artificial neural network models have similar levels of performance in classifying the outcome of violence but in practical terms, a decision tree model is more interpretable than an artificial neural network model.

Keywords: Decision tree; naive Bayes; artificial neural network; logistic regression; violence in Thailand. (search for similar items in EconPapers)
JEL-codes: C53 C55 C88 N35 (search for similar items in EconPapers)
Date: 2019
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