## 3 Material and Methods
### Evaluation Metrics  
The performance of methods for identifying confirmed and discarded COVID-19 cases is evaluated using specific metrics. The true and false labels are derived from the gold standard, with prediction results represented as the (i) true positive (\(TP\)) rate, (ii) true negative (\(TN\)) rate, (iii) false positive (\(FP\)) rate, and (iv) false negative (\(FN\)) rate. \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Technique** & **Description** \\ \hline Random sampling [72] & Two options: undersampling and oversampling. In undersampling, instances from the majority class are discarded until balance is achieved. In oversampling, minority class data are duplicated to balance both classes. \\ \hline SMOTE (synthetic minority oversampling technique) [73] & Synthetic data are generated by selecting a minority class instance and identifying its nearest neighbor. A new synthetic instance is created by combining these two instances. \\ \hline ADASYN synthetic algorithm [74] & Uses density estimation to create synthetic samples for the minority class. First, the required amounts of data are calculated, and then k-nearest neighbors are used to generate synthetic data based on density. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of sampling techniques for handling imbalanced data. \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Technique** & **Description** \\ \hline Balanced bootstrap classifier [70] & This method combines bagging and balancing techniques to address class imbalances. It uses a balanced sampling strategy to ensure that each bootstrap sample contains a more equal representation of each class, enhancing the classifier’s ability to identify minority class instances. \\ \hline Balanced random forest [71] & This classifier creates balanced bootstrap samples from both majority and minority classes. Each decision tree is trained on a representative sample, enhancing its ability to handle imbalanced classifications. By focusing on balanced sampling, this approach helps ensure that minority classes are adequately represented during training, leading to more reliable and unbiased predictions. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cost-sensitive learning techniques. Key metrics for evaluating the classifier's ability to distinguish confirmed from discarded cases are derived from the confusion matrix in Table 7.  
Table 8 outlines the study's evaluation metrics with formulas and definitions. Some metrics highlight the study's discriminatory nature, while others reflect predictive capabilities [75]. In medical diagnostics, these metrics pose challenges when applied to imbalanced data. For example, accuracy is a common measure of classification performance, but it does not always capture the biological significance of ML results, especially in diagnosis, where expert review is necessary for final interpretation. This section details a comprehensive approach, detailing data cleansing, feature selection, machine learning algorithms, and resampling techniques. However, to strengthen replicability, further details are provided on specific procedures and choices made throughout the process. For data cleansing, imputation techniques were used to handle missing data, ensuring completeness without compromising data integrity. Precise hyperparameter values optimize model performance and are shown in Table 9. This transparency enables accurate replication of the study's approach and findings. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Real Confirmed Subjects Real Discarded Subjects**} \\ \hline Predicted confirmed class & TP & FP \\ \hline Predicted discarded class & FN & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A confusion matrix for binary classification.