###### Abstract  
Smoking is one of the major public health issues, which has a significant impact on premature death. In recent years, numerous decision support systems have been developed to deal with smoking cessation based on machine learning methods. However, the inevitable class imbalance is considered a major challenge in deploying such systems. In this paper, we study an empirical comparison of machine learning techniques to deal with the class imbalance problem in the prediction of smoking cessation intervention among the Korean population. For the class imbalance problem, the objective of this paper is to improve the prediction performance based on the utilization of synthetic oversampling techniques, which we called the synthetic minority over-sampling technique (SMOTE) and an adaptive synthetic (ADASYN). This has been achieved by the experimental design, which comprises three components. First, the selection of the best representative features is performed in two phases: the lasso method and multicollinearity analysis. Second, generate the newly balanced data utilizing SMOTE and ADASYN technique. Third, machine learning classifiers are applied to construct the prediction models among all subjects and each gender. In order to justify the effectiveness of the prediction models, the f-score, type I error, type II error, balanced accuracy and geometric mean indices are used. Comprehensive analysis demonstrates that Gradient Boosting Trees (GBT), Random Forest (RF) and multilayer perceptron neural network (MLP) classifiers achieved the best performances in all subjects and each gender when SMOTE and ADASYN were utilized. The SMOTE with GBT and RF models also provide feature importance scores that enhance the interpretability of the decision-support system. In addition, it is proven that the presented synthetic oversampling techniques with machine learning models outperformed baseline models in smoking cessation prediction. smoking; class imbalance; synthetic oversampling; machine learning; decision making; feature importance +
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to approximately 10 million by the year 2020 [1; 2]. The WHO report noticed that half of the world's smokers are alive today. However, they are more likely to die prematurely due to tobacco-related disease if they continue to smoke. Indeed, tobacco contains high addictive substance namely nicotine, which is the leading cause of smoking dependence. Nicotine addiction can be strongly associated with a variety of psychiatric diagnoses, cancers, stroke, and chronic disorders. On the other hand, smoking cessation reduces the chance of cancer risk such as lung, larynx, esophagus, and pancreas among smokers [3; 4; 5; 6; 7]. Most tobacco users make multiple quit attempts of smoking over their lifetime and regret having started. Nevertheless, the immensely addictive nature of nicotine makes it hard to quit smoking dependence for most smokers without effective interventions [8; 9]. Due to its complexity, many countries have been realizing to decrease tobacco consumption in the care of planning and implementing smoke-free ways. Thus, there remains a need to construct an efficient decision support system, thereby assisting in smoking cessation intervention for determining whether to quit smoking or not. It is widely known that machine learning techniques have been efficiently used in the healthcare area, including disease and risk prediction [10; 11; 12; 13]. However, decision-making responses suffer from the class imbalance problem. The class imbalance problem has received much attention from researchers in the fields of medical [14], fraud detection [15] and bankruptcy prediction [16]. To deal with this problem, advanced data-driven and machine learning techniques have been developed constantly. The sampling techniques are investigated to rebalance an imbalanced dataset to alleviate the effect of the skewed class distribution. Broadly, sampling techniques can be classified into two groups, such as under-sampling and over-sampling [17].