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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2019 Vol.9(1): 83-90 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.769

Prediction of 6 Months Smoking Cessation Program among Women in Korea

Khishigsuren Davagdorj, Seon Hwa Yu, So Young Kim, Pham Van Huy, Jong Hyock Park, and Keun Ho Ryu
Abstract—Cigarette smoking is the leading cause of preventable death in a general population and it seems a significant topic in health research. The primary aim of this study determines the significant risk factors and investigates the prediction of 6 months smoking cessation program among women in Korea. In this regard, we examined real-world dataset about a smoking cessation program among the only women from Chungbuk Tobacco Control Center of Chungbuk National University College of Medicine in South Korea which collected from 2015 to 2017. Accordingly, we carried out to compare four machine learning techniques: Logistic regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) in order to predict response for successful or unsuccessful smoking quitters. Totally we analyzed 60 set of features that may affect the association between smoking cessation such as socio-demographic characteristics, smoking status for the age of starting, duration and others by employing a filter-based feature selection method. Respectively, we identified significant 8 factors which associated with smoking cessation. The experimental results demonstrate that NB performs better than other classifiers. Moreover, the performance of prediction models as measured by Accuracy, Precision, Recall, F-measure and ROC area. This finding has gone some way towards enhancing our better understanding of the significant factors contributing to smoking cessation program implementation and accompanying to concern public health.

Index Terms—Smoking cessation, women, feature selection, logistic regression, support vector machine, random forest, Naïve Bayes.

Khishigsuren Davagdorj is with the Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, South Korea (e-mail: suri@dblab.chungbuk.ac.kr).
Seon Hwa Yuand So Young Kim are with the Chungbuk Tobacco Control Center, Chungbuk National University, South Korea (email: tocjstk1256@gmail.com, sykim@gmail.com).
Pham Van Huy is with the Faculty of Information Technology of Ton Duc Thang University, Vietnam (e-mail: phamvanhuy@tdt.edu.vn).
Jong Hyock Park is with the Chungbuk National University College of Medicine, South Korea, and Chungbuk Tobacco Control Center, Chungbuk National University, South Korea (email: jonghyock@gmail.com).
Keun Ho Ryu is with the Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam and Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, South Korea (corresponding author: Keun Ho Ryu; e-mail: khryu@tdtu.edu.vnr, khryu@.chungbuk.ac.kr).

[PDF]

Cite: Khishigsuren Davagdorj, Seon Hwa Yu, So Young Kim, Pham Van Huy, Jong Hyock Park, and Keun Ho Ryu, "Prediction of 6 Months Smoking Cessation Program among Women in Korea," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 83-90, 2019.

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