## 1 Introduction  
With the ongoing development in information and communication technologies, unprecedented amounts of data have been generated in multiple fields of healthcare [1]. Data-driven models offer great research opportunities, allowing us to extract clinical knowledge and support decision-making. Along with data proliferation, the use of Artificial Intelligence (AI) has been intensified in recent years fostered by advances in computer processing, software platforms, and automatic differentiation [2]. Within AI, Machine Learning (ML) methods have attracted significant attention in both academia and industry, being used in multiple domains ranging from computer vision to natural language processing [3] and for different tasks such as classification, regression, and clustering, among others. Despite the potential of ML methods, most of them are generally hampered by the class imbalance problem. It occurs when the proportion of samples of one class greatly outnumbers the others in the learning process [4; 5]. Most ML algorithms are built to work properly in scenarios where the number of samples per class is fairly equal, since learning with imbalanced datasets biases the classifier towards the majority class. To deal with this, several methods have been proposed in the literature [4; 5; 6; 7], which can be classified into two types, algorithmic-level and data-level approaches. The former adapts the loss function of the algorithm by assigning a higher weight to the misclassification of samples associated with the minority classes during the training process [8]. Examples of these approaches include cost-sensitive learning and ensemble methods [8]. In contrast, data-level approaches balance the class distribution by undersampling the majority class, by oversampling the minority classes, or by considering a hybrid approach that combines undersampling and oversampling approaches [9]. In this paper, we primarily focus on oversampling techniques. Among them, Synthetic Minority Oversampling Technique (SMOTE) [10] is one of the most used. SMOTE relies on the nearest neighbors technique, aiming to generate new samples that are mid-way between the nearest neighbors in any particular class. SMOTE has been used to generate numerical data and improve the generalization of predictive models in tasks such as regression and classification [11; 12; 13]. However, many real-world applications present high-dimensional and heterogeneous data (mixed-type) with numerical and categorical features. SMOTEN and its variants have been used in several clinical works for improving the predictive performance in identification or prevention of diseases, such as cervical cancer prevention using risk factor inputs [14], sepsis early prediction using both structured and unstructured data [15], COVID-19 identification using chest X-ray images [16], and smartphone-based cough recordings [17] or the cardiovascular monitoring of COVID-19 patients by using wearable medical devices [18]. Recently, generative models based on Artificial Neural Networks (ANNs) have revolutionized the outcomes in multiple knowledge areas due to their outstanding performance for creating synthetic data, particularly in computer vision and image applications [19; 20]. Despite these outcomes, a few strategies have been studied for generating tabular (structured) data. For instance, a variant of the Variational Autoencoder (VAE) called Tabular VAE (TVAE) has been proposed, which uses two ANNs and trains using evidence lower-bound loss with the goal of creating mixed-type data [21]. Additionally, the techniques based on Generative Adversarial Networks (GANs) have emerged as a potential tool for creating synthetic data, frequently enhancing the model's performance in classification tasks, while also addressing data privacy issues. Although the application of GANs has been validated in different domains [19], they have not been well studied when considering electronic health records (EHRs) with structured and categorical and continuous data [22].