## 1 Introduction
Because tabular data typically contain a mix of categorical and continuous features, generating realistic synthetic data is not an easy task. In this sense, Conditional Tabular GANs (CTGANs) have been created for modeling tabular data distributions and sampling entries from them [21] by employing a conditional generative antagonistic network. Furthermore, in several real-world datasets, CTGAN has outperformed Bayesian approaches [21]. In healthcare applications, class imbalance is a recurrent challenge to building predictive models with a reasonable generalization capacity, because a highly skewed distribution of training data will be prone to forcing the learning algorithm to be biased towards the majority class. In particular, we focused our research on cardiovascular diseases (CVDs) since they are the most significant cause of death worldwide [23]. Specifically, we analyzed data collected with a smartphone-based method from a population group in Norway [24]. The dataset comprised a series of survey questions related to socioeconomic factors, alcohol, and drug use, physical activity (PA), dietary intake, and one question indicating current/previous non-communicable diseases. Working with categorical features in MLis challenging due to most algorithms working adequately with numerical data, one-hot encoding being one of the most popular approaches to transforming categories into numbers [25]. However, this approach generally returns a sparse matrix, increasing the number of features (dimensions) that the model handles, and the risk of the curse of dimensionality problem [26]. Furthermore, when the feature includes an excessive number of categories, the majority of which are irrelevant to the prediction, this is amplified. To cope with these issues, a target encoding strategy is used [25], which aims to encode the categories by replacing them with a measurement of the impact that they may have on the target. This research aimed to perform a comparative study of synthetic categorical data generation methods, with a special focus on GAN-based models. To this end, we have generated new samples with oversampling methods that seek to maintain the same feature categories as the original data, a similar probability distribution of attributes and the dependence between them, thus addressing the problem of data imbalance. All of this enables the enhancement of the effectiveness and accuracy of the developed classifiers. However, some ML methods use nonlinear transformations, leading to a lack of interpretability and creating black-box models [27]. Interpretation is defined as the process of generating human-understandable explanations of outcomes provided by computational models [27; 28]. Several approaches have been proposed for gaining interpretability for improving model understandability and reliability, highlighting _model-specific_ and _model-agnostic_ methods [29]. The former is based on feature weighting, which seeks to identify the contributions of the features that determine the predictions of ML models. Although feature weighting is easy to apply to simple linear models, these models tend to have limited predictive performance and, therefore, also have limited interpretive relevance. The second approach outlined above, the model-agnostic approach, appears to address this limitation, which aims to extract post-hoc explanations by treating the original model as a black box. Concerning the model interpretability, among the most popular interpretable models, the generalized linear and tree-based models are of great value to interpreting model predictions [30; 31]. In this work, two linear models were considered: Least Absolute Shrinkage and Selection Operator (LASSO) [32] and Linear Support Vector Machine (SVM) [33]. The goal was to extract the most relevant features by analyzing the weights of the coefficients of each of the features to give information about their significance for predicting the output class. As a nonlinear model, a Decision Tree (DT) was considered since it provides the importance of each feature [34].