Title
Predictive Models for Classifying the Outcomes of Violence Case Study for Thailand’s Deep South
Authors
Abstract
Violence is now widely recognized as a public health problem because of its significantconsequences 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.
in Thailand.
Keywords
Decision tree, naive Bayes, artificial neural network, logistic regression, violence
Classification-JEL
C53, C55, C88, N35
Pages
56-92