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: email@example.com).
Seon Hwa Yuand So Young Kim are with the Chungbuk Tobacco Control Center, Chungbuk National University, South Korea (email: firstname.lastname@example.org, email@example.com).
Pham Van Huy is with the Faculty of Information Technology of Ton Duc Thang University, Vietnam (e-mail: firstname.lastname@example.org).
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: email@example.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: firstname.lastname@example.org, khryu@.chungbuk.ac.kr).
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.