## 5 Discussion  
The results of this study confirm that for classification problems with imbalanced data, such as COVID-19 diagnosis based on patient symptoms, applying imbalance algorithms significantly enhances the performance of ML techniques. These findings align with the existing literature, which underscores the importance of addressing class imbalance to develop effective predictive models [40, 41]. Algorithms like ROS, RUS, SMOTE, and ADASYN consistently improve classifier performance in imbalanced datasets. ROS notably enhanced the F1 score, while SMOTE and ADASYN achieved a good balance between recall and precision. However, the impact varies by classifier type and data imbalance nature. RUS, for example, displayed modest performance, achieving the balance between precision and recall, though it did not reach the improvement levels seen with ROS or SMOTE. In the imbalanced case (i.e., without balancing techniques), the high LR+ and DOR values suggest that not applying sampling techniques may sometimes suffice, especially when maximizing class discrimination. This finding indicates that while imbalance techniques are generally beneficial, models without adjustment can still perform competitively, depending on the dataset structure and characteristics. The best combination of imbalance techniques and classification algorithms depends on the specific objective, such as maximizing sensitivity, specificity, or F1 score. For example, MLP, combined with ADASYN or SMOTE, achieved a high F1 score, sensitivity, and AUC, particularly benefiting highly imbalanced datasets. On the other hand, SVM, in combination \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Accuracy** & **F1** & **Sens** & **Spec** & **LR+** & **AUC** & **DOR** & **CK** \\ \hline XGB & 0.878 (0.002) & 0.298 (0.031) & 0.215 (0.029) & 0.969 (0.002) & 6.887 (0.443) & 0.591 (0.014) & 8.528 (0.860) & 0.620 (0.015) \\ MLP & 0.887 (0.007) & 0.287 (0.002) & 0.187 (0.010) & 0.984 (0.005) & 12.816 (4.827) & 0.584 (0.007) & 15.600 (6.144) & 0.621 (0.012) \\ NB & 0.850 (0.008) & 0.320 (0.011) & 0.288 (0.004) & 0.928 (0.007) & 4.044 (0.458) & 0.607 (0.006) & 5.280 (0.665) & 0.618 (0.008) \\ DT & 0.829 (0.004) & 0.312 (0.028) & 0.322 (0.043) & 0.898 (0.006) & 3.159 (0.298) & 0.609 (0.020) & 4.223 (0.663) & 0.606 (0.015) \\ SVM & 0.780 (0.043) & 0.214 (0.130) & 0.247 (0.146) & 0.853 (0.034) & 1.924 (1.498) & 0.549 (0.084) & 2.726 (2.648) & 0.544 (0.076) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification metrics for the original. with ROS, has proven to be more suitable for maximizing sensitivity, which is crucial in applications where the detection of positive cases is paramount. Each classifier benefits differently from specific imbalance algorithms. For instance, MLP achieves substantial sensitivity gains with SMOTE, ROS, and ADASYN, reaching an optimal sensitivity-specificity balance. Although SVM also improves with these techniques, it does so less than random forest. AdaBoost, by contrast, responds best to ADASYN, showing significant sensitivity improvement. However, the benefits of imbalance algorithms are not uniform across classifiers. Techniques like SMOTE and ROS yield consistent improvements, especially in AdaBoost, whereas RUS shows modest gains in models like Naive Bayes. This variability highlights the importance of selecting the imbalance algorithm based on the specific classifier and the dataset's characteristics. The imbalance in sensitivity and specificity performance is a critical issue, especially in clinical applications. Some models, such as XGBoost and MLP, exhibit high specificity but low sensitivity, which is problematic in contexts requiring early detection. This underscores the need to balance both metrics to ensure a more reliable and effective diagnosis, especially in situations where missing positive cases could have serious consequences.