## 4 Discussion  
In this work, different resampling methods were used, highlighting the use of CTGANs as an oversampling technique to generate synthetic data for achieving data balance among different classes in a classification problem. In particular, a dataset with real-world data from healthy individuals and individuals with CVD was employed to carry out the synthetic data generation. Different metrics for quality assessment of the generated synthetic datasets were analyzed and discussed. Our results demonstrated the high potential of CTGANs for generating categorical data, keeping relevant information, and improving classification performance. The following five findings are particularly important to this study. Firstly, the GAN-based model generates high-quality synthetic data, yielding a PMF that is very close to that estimated using real data. This makes a significant contribution to the literature, showing the potential of GANs in the clinical setting [21]. Secondly, it has been demonstrated that linear classifiers outperform nonlinear ones when using target encoding. Thirdly, the combination of the GAN and LASSO-based models yields an AUC value of 71%. Finally, because of the interpretive capabilities of our models, the findings of this study may help to improve both the prediction and prevention of CVD and the knowledge of the risk factors related to CVD. The most important risk factors for CVD prediction identified in all models were the presence of high cholesterol, age, BMI, and sex. The previous findings are consistent with the primary CVD risk factors identified in the literature [61; 62; 63]. According to [61], BMI is one of the most critical criteria to consider since excessive adiposity is a significant risk factor for morbidity and death from type 2 diabetes, CVD, and different types of cancer. On the one hand, individuals from high-income countries are more likely to consume healthy foods, according to [64]. On the other hand, low-income people tend to consume more fat and less fiber, which explains the average importance of characteristics such as fish or meat consumption in the prediction of CVD. Furthermore, individuals in low- and middle-income countries are more likely to drink alcohol and smoke than socioeconomic groups in high-income countries, which explains the importance of these features for predicting CVD, according to [65]. Despite the potential benefits of using clinical data for research, these data are highly sensitive, and their use is restricted by privacy legislation and organizational guidelines [66]. Additionally, patient data are regulated by laws protecting patients' privacy such as the _Health Insurance Portability and Accountability Act_ in the United States and the _General Data Protection Regulation_ in the European Union [67]. The sharing of public health data has  
Figure 8: SHAP summary plot of KNN model using the CTGAN oversampling technique and all subjects as well as their impact on the prediction. Only the 20 most relevant features are shown. always been hampered by privacy concerns. Furthermore, in the clinical setting, most of the populations studied are commonly unbalanced, with the class of patients with a certain disease typically being smaller than the class of healthy individuals. In this way, synthetic data could allow researchers to delve deeper into complex medical issues, eliminating challenges such as a lack of access to protected data and addressing the issue of class imbalance [49]. As discussed throughout the previous sections, one of the main challenges when working with categorical data is to conduct an appropriate encoding process. In a prior author's work [68], one-hot encoding was evaluated, while in the current paper, target-encoding was considered. Target-encoding with regularization was shown to be an efficient technique for encoding categorical features, which do not increase the dimensionality in the analysis and allow us to achieve better results in predictive classification metrics.