## 3 Material and Methods
### Research Methodology  
The proposed methodology for classifying the imbalanced EPVIGILA dataset is illustrated in Figure 1.  
Figure 1: The methodology for addressing classification imbalance in the EPVIGILA dataset.  
The process begins by cleaning data to remove inconsistencies and converting the dataset into a suitable analysis format. This step reveals patterns in symptoms and comorbidities associated with confirmed and discarded cases, facilitating binary classification. These diagnostic features are then encoded from categorical to numerical data so they can be interpreted by ML algorithms.  
After encoding and class separation, the dataset is divided into training and test sets. The imbalanced training set trains various models to distinguish confirmed from discarded cases. After training, the models are tested using the test set to assess their performance in the different experimental configurations.  
For the experiments, traditional ML algorithms, including support vector machine (SVM), multilayer perceptron (MLP), Naive Bayes (NB), decision tree (DT), and extreme gradient boosting (XGBoost), were employed as popular methods [51]. Initially, the models were trained on the imbalanced training set, followed by the application of sampling strategies to artificially balance the dataset. These techniques included random oversampling (ROS), random undersampling (RUS), synthetic minority oversampling technique (SMOTE), and adaptive synthetic (ADASYN) sampling algorithm [52]. The use of sampling techniques enhanced the model performance by balancing class representation and improving generalization. SMOTE generated synthetic examples to avoid overfitting, while ADASYN focused on challenging cases, increasing sensitivity. ROS--applied after feature scaling--ensured balanced exposure to both classes, boosting precision, recall, and the F1 score, especially for COVID-19-positive and high-risk patients.  
Additionally, using the imbalanced training set, ensemble learning techniques such as adaptive boosting (AdaBoost) and bootstrap aggregation (bagging) were employed, both of which are recognized as state-of-the-art methods [53]. Cost-sensitive techniques, including the cost-sensitive bootstrap and cost-sensitive random forest, were also applied [54]. These approaches are designed to manage the imbalanced data by assigning different weights to the classes, improving performance in minority classes without requiring sampling techniques. The trained models were then evaluated using the test set.  
The performances of the models were evaluated using various metrics, such as accuracy, sensitivity, specificity, LR+, DOR, and AUC. These metrics help assess how well the models classify the data correctly. The results are categorized as confirmed or discarded based on their performance relative to the evaluation metrics. This approach ensures that only the most reliable and accurate models are selected for further use or implementation. The entire process is visually presented, showing the path from data preprocessing to model evaluation, providing a clear and organized method for handling unbalanced datasets and performing binary classification [55].  
In summary, the integration of ML methods with sampling techniques effectively addresses the EPIVIGILA dataset's imbalance, enhancing diagnostic metrics. Sampling methods helped improve class representation, while cross-validation and regularization mitigated challenges like overfitting. Together, these techniques strengthened the model's capacity to reliably identify high-risk COVID-19 cases, supporting more robust clinical decision-making.