###### Abstract
Under-sampling discards the samples from the majority class to make it equal to the minor class. A drawback of under-sampling is the loss of information. On the other hand, over-sampling creates the samples in minority class to make it equal to the majority class. Moreover, it has a drawback, which is associated with duplicated random records, which can be a cause of overfitting. Existing studies have exhibited that the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic (ADASYN) over-sampling techniques are used rather than downsizing dataset [18; 19; 20]. Therefore, we conduct an empirical comparison based on machine learning methods to predict the success of smoking cessation, having real-world smoking cessation interventions dataset collected from the Korea National Health and Nutrition Examination Survey (KNHANES). Accordingly, we aim to solve the class imbalance problem where utilizing synthetic oversampling techniques, namely SMOTE and ADASYN. Generally, our experimental design is composed of the following components: First, a lasso and multicollinearity--based feature selection approach is addressed to eliminate irrelevant features. Next, SMOTE and ADASYN techniques are applied to create new synthetic data for balancing the classes among all subject, men and women. Finally, widely used logistic regression (LR), multilayer perceptron (MLP), deep MLP, Random Forest (RF), Gradient Boosting Trees (GBT), k nearest neighbors (KNN) and support vector machine (SVM) classifiers are utilized to build the prediction models in imbalanced and balanced datasets. Comparison analyses determine the proper setting of hyper-parameters for minimizing the classification error. The non-parametric Friedman test is applied to ensure the differences between performances of prediction models. Moreover, SMOTE with GBT and RF models are provided feature importance scores in order to enhance the interpretability of the decision support system in smoking cessation. Overall, the principal contributions of this study are as follows:  
* Provide a comparative analysis of machine learning methods based on experimental design to determine their predictive performances when used to predict smoking cessation intervention among the Korean population. * Experimental results prove that the integration of synthetic oversampling techniques with machine learning classifiers enhance the evaluation performance among all subjects and each gender. * Important features are defined in terms of lasso and multicollinearity analysis, which can be a substantial benefit for understanding the causes of smoking risk factors. * SMOTE with GBT and RF models are provided feature importance scores to enhance the model interpretability. * Empirically, practical models have gone some way towards solving implication for practice class imbalanced problem, and these findings would be significantly important for the public health impact has arisen from the quit smoking. The remainder of this paper is organized as follows: Section 2 introduces the literature review of smoking cessation. Section 3 presents our proposed experimental design, including the building procedure and experimental configuration in detail. In Section 4, the dataset and overall experimental results are provided. Section 5 presents the discussion of this study. Finally, the study is concluded in Section 6.