Synthetic oversampling based decision support framework to solve class imbalance problem in smoking cessation program

Synthetic oversampling based decision support framework to solve class imbalance problem in smoking cessation program

Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed quit attempts. Predicting the likelihood of success in smoking cessation program is necessary for public health. In recent years, a few numbers of decision support systems have been developed for dealing with smoking cessation based on machine learning techniques. However, the class imbalance problem is increasingly recognized as serious in real-world applications. Therefore, this paper presents a synthetic minority over-sampling technique (SMOTE) based decision support framework in order to predict the success of smoking cessation program using Korea National Health and Nutrition Examination Survey (KNHANES) dataset. We carried out experiments as follows: I) the unnecessary instances and variables have been eliminated, II) then we employed three variations of SMOTE, III) also the prediction models have been constructed. Finally, compare the prediction models to obtain the best model. Our experimental results showed that SMOTE improved the prediction performance of machine learning classifiers among evaluation metrics. Moreover, SMOTE regular based Random Forest (RF) and Naïve Bayes (NB) classifiers were determined the best prediction models in real-world smoking cessation dataset. Consequently, our decision support framework can interpret the important risk factors of smoking cessation using multivariate regression analysis.