Prediction of 6 months smoking cessation program among women in Korea. Davagdorj, K., Yu, S. H., Kim, S. Y., Van Huy, P., Park, J. H., & Ryu, K. H. International Journal of Machine Learning and Computing, 9(1):83–90, 2019. doi abstract bibtex 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.
@article{Pham2019,
title = {Prediction of 6 months smoking cessation program among women in {Korea}},
volume = {9},
issn = {20103700},
doi = {10.18178/ijmlc.2019.9.1.769},
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.},
number = {1},
journal = {International Journal of Machine Learning and Computing},
author = {Davagdorj, Khishigsuren and Yu, Seon Hwa and Kim, So Young and Van Huy, Pham and Park, Jong Hyock and Ryu, Keun Ho},
year = {2019},
keywords = {Feature selection, Logistic regression, Naïve Bayes, Random forest, Smoking cessation, Support vector machine, Women},
pages = {83--90},
}
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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. 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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.},\n\tnumber = {1},\n\tjournal = {International Journal of Machine Learning and Computing},\n\tauthor = {Davagdorj, Khishigsuren and Yu, Seon Hwa and Kim, So Young and Van Huy, Pham and Park, Jong Hyock and Ryu, Keun Ho},\n\tyear = {2019},\n\tkeywords = {Feature selection, Logistic regression, Naïve Bayes, Random forest, Smoking cessation, Support vector machine, Women},\n\tpages = {83--90},\n}\n\n","author_short":["Davagdorj, K.","Yu, S. H.","Kim, S. Y.","Van Huy, P.","Park, J. H.","Ryu, K. 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