## 4 Results  
The following results show the performance of ML models applied to the EPIVIGILA dataset, before and after implementing different data balancing techniques. Key metrics like precision, sensitivity, specificity, and F1 score are compared to assess how these balancing methods improve classification in the context of imbalanced data. Figure 4 depicts the distribution of confirmed and discarded COVID-19 patients. As shown, 86% of the cases were discarded. The figure illustrates the percentage distribution of discarded and confirmed COVID-19 cases in the EPIVIGILA dataset post-data cleaning. Discarded cases indicate ruled-out COVID-19, while confirmed cases represent positive diagnoses. The high percentage of discarded cases underscores a significant class imbalance, which is common in epidemiological data. Addressing this imbalance is crucial for accurate model training and evaluation in this study. Figure 5 displays classification metrics for each ML method. The labels 'imbalanced' (without applying sampling methods), RUS, ROS, SMOTE, and ADASYN reflect the average performances of SVM, MLP, NB, DT, and XGBoost after applying these sampling techniques. Sampling was not used with ensemble methods (bagging and AdaBoost) or cost-sensitive approaches (boosting and random forest). The imbalanced baseline model showed moderate accuracy and a low F1 score due to poor recall. ROS produced the highest F1 score and sensitivity, while RUS maintained a balanced F1 score. SMOTE and ADASYN achieved good sensitivity and F1 scores, but AdaBoost and bagging performed poorly. Cost-sensitive methods showed moderate performance, with ROS emerging as the most effective, followed by SMOTE and ADASYN. Both enhanced sensitivity by generating synthetic samples, with ADASYN specifically targeting harder-to-classify cases. This analysis presents a detailed comparison of classification metrics--accuracy, sensitivity, specificity, \begin{table}
\begin{tabular}{l c c c} \hline
**Method** & **Training Time (s)** & **Test Time (ms/Record)** & **Scalability Rating** \\ \hline XGBoost & 180 & 2.5 & High \\ \hline MLP & 120 & 3.2 & High \\ \hline SVM & 100 & 1.8 & High \\ \hline Naïve Bayes & 30 & 0.9 & Very High \\ \hline Decision Trees & 50 & 1.2 & High \\ \hline AdaBoost & 300 & 5.0 & Medium \\ \hline Bagging & 350 & 4.8 & Medium \\ \hline CS Random forest & 400 & 6.2 & Medium \\ \hline CS bagging & 420 & 6.5 & Medium \\ \hline \end{tabular}
\end{table} 
Table 10: Computational costs for training and testing across methods. Figure 4: Real distribution of EPIVIGILA data. F1 score, and AUC-ROC--across different machine learning models and sampling techniques. These metrics provide a multi-dimensional view of model performance, essential for understanding the impact of data imbalance on predictive accuracy. In particular, recall and F1 score are emphasized because they are crucial for identifying true positive cases in an imbalanced dataset. Figure 6 displays the performance of various ML methods, evaluated using the positive likelihood ratio (LR+) and diagnostic odds ratio (DOR). The imbalanced dataset, without sampling, displayed average LR+ and DOR, reflecting standard performance. AdaBoost delivers the best results, while ROS and bagging demonstrate moderate effectiveness. In contrast, RUS shows the lowest scores, indicating limited effectiveness. SMOTE, ADASYN, CS.Boosting, and CS.RF offer moderate improvements. Overall, AdaBoost stands out as the most effective, with other techniques showing varying degrees of success in improving model performance. This research shows that applying sampling methods significantly enhances performance metrics, particularly sensitivity and diagnostic measures. We addressed the challenges of unbalanced data in COVID-19 diagnosis using ML algorithms. By evaluating various approaches and integrating sampling techniques, we observed notable improvements in sensitivity. These results highlight the importance of addressing data imbalances  
Figure 5: Classification metrics. Figure 6: Diagnosis metrics.