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http://dx.doi.org/10.7465/jkdi.2017.28.6.1337

Developing the high-risk drinking predictive model in Korea using the data mining technique  

Park, Il-Su (Department of Health Management, Uiduk University)
Han, Jun-Tae (Department of Student Aid Policy Research, Korea Student Aid Foundation)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.6, 2017 , pp. 1337-1348 More about this Journal
Abstract
In this paper, we develop the high-risk drinking predictive model in Korea using the cross-sectional data from Korea Community Health Survey (2014). We perform the logistic regression analysis, the decision tree analysis, and the neural network analysis using the data mining technique. The results of logistic regression analysis showed that men in their forties had a high risk and the risk of office workers and sales workers were high. Especially, current smokers had higher risk of high-risk drinking. Neural network analysis and logistic regression were the most significant in terms of AUROC (area under a receiver operation characteristic curve) among the three models. The high-risk drinking predictive model developed in this study and the selection method of the high-risk intensive drinking group can be the basis for providing more effective health care services such as hazardous drinking prevention education, and improvement of drinking program.
Keywords
Decision tree; high-risk drinking; logistic regression; neural network;
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Times Cited By KSCI : 3  (Citation Analysis)
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