## 2 Materials and Methods
### Modelling  
After data processing using the SMOTE and Spread Subsample techniques, several models were built using the WEKA Explorer module utilizing different supervised machine learning algorithms, namely, J48 DT, RF, SVM, k-NN, and NB. In the Classify tab of the WEKA Explorer, the researchers chose the classifier name, and selected 10-fold cross-validation. To determine the best configuration for each algorithm, the researchers performed hyperparameter optimization by performing several trainings with the same dataset for each algorithm. The researchers used 10-fold cross-validation testing and a batch size of 100 for all the experiments. For the J48 DT, the researchers used two as the minimum number of instances per leaf for all the training processes. The researchers also used a confidence factor for pruning, and unpruned either true or false as the parameters to be tuned. For the RF, the researchers set  
Figure 5: The Preprocess tab of WEKA Explorer showing the result of balancing the dataset through Spread Subsample or undersampling the majority class to make it equal to the minority class. the maximum depth to zero, which means unlimited depth, and the number of iterations to 100, which is the number of trees in the forest. Bagsize is the parameter that the researchers tuned to determine the best bag size that will yield good results. For the SVM, the C and Kernel hyperparameters were used. The C is the complexity parameter, which determines how flexible the process is in determining the line that separates the classes, and the Kernel determines how the data will be separated, either by a straight line, a curved line, a polygonal or a shape similar to that used in [26]. In building the model using the k-NN algorithm, the distance function used was Euclidean distance, and the hyperparameters tuned were the KNN or the number of neighbors to use and the Cross-Validate parameter, which indicates whether a cross-validation will be used in determining the best k value. Lastly, for the NB algorithm, the hyperparameters used were whether the process would use Kernel Estimator and Supervised Discretization rather than the normal distribution of numeric attributes. Each algorithm underwent several experimental trainings using the WEKA Explorer Classify tab. For each training, the performance of the developed model was displayed in the classifier output section. An example of the classifier tab with the details of the developed models is displayed in Figure 6.  
In Figure 6, the Classify tab is displayed. When the user clicks the choose button, the available machine learning algorithm classifiers will be displayed. Several folders with a list of algorithms to be used will be presented, and both J48 DT and RF can be found under the trees folder, while NB is under the Bayes folder, SVM belongs to the functions folder and k-NN can be seen in the lazy folder. When the desired machine learning algorithm was selected, the test option needs to be filled out; in this study the researchers utilized 10-fold  
Figure 6: The Classify tab of WEKA Explorer wherein the user can choose different algorithms to be applied to the dataset. The details concerning the developed model’s performance were displayed in the classifier output section. cross-validation. Just below the test options section, the class attribute, or the attribute to be predicted, needs to be selected. In this case, the attribute \"COVID-19\" was the class attribute. To tune the hyperparameters, the label beside the choose button must be clicked to allow the users to input the desired configurations. Lastly, the start button needs to be clicked to start the training process to build the model. To provide a visual representation of building the model, a knowledge flow diagram was devised using the Knowledge Flow Module. The knowledge flow in building the model is displayed in Figure 7.  
In Figure 7, the CSVLoader reads the dataset in comma-separated format, then an attribute needs to be designated as the class attribute; this can be done by using the class assigner.