## 2 Materials and Methods
### Data Processing  
To process the dataset, the authors used the WEKA machine learning software's Explorer and Knowledge Flow applications, and the main page of WEKA is presented in Figure 2.  
There are various document formats accepted in WEKA, such as arff, C4.5, csv, JSON file, etc., and the COVID-19 Symptoms and Presence dataset is in csv format, which makes it easy to be imported and analyzed in the software. Data preprocessing can be started by clicking the open file and browsing for the location of the dataset. Once the dataset is imported in WEKA, the current relation, attributes, instances and the sum of weights and visualizations pertaining to the dataset will be displayed. The Preprocess tab in the WEKA Explorer application is displayed in Figure 3.  
Once an attribute is selected, values such as name; missing, distinct and unique values; data type; labels; count, and weights will be visible. Distribution between the class labels is also displayed in a bar chart by clicking the COVID-19 attribute seen on the left side of the form. For the COVID-19 Symptoms and Presence dataset, the classes have a 4:1 class imbalance. The researchers used the synthetic minority oversampling technique (SMOTE) to generate additional instances for the minority group, which is the class labeled \"No\". SMOTE oversamples the minority class by generating additional synthetic samples, and in this way, the class with fewer samples will be increased. Combining the method of oversampling the minority class and undersampling the majority class, or cutting off some samples in the class containing more samples, will yield better classifier performance than just undersampling the majority class [24]. The SMOTE can be used in WEKA by installing it using the Package Manager under the Tools menu of the WEKA GUI Chooser. After installation, the package can be found by opening the Filters folder, then the Supervised folder, and the Instance folder. The details of the updated dataset can be seen in Figure 4.  
Figure 2: The main page of WEKA displaying the five modules including Explorer, Experimenter, Knowledge Flow, Workbench and Simple CLI.  
Figure 4: The Preprocess tab of WEKA Explorer showing the result of balancing the dataset through SMOTE using a bar graph for representing the number of instances per class.  
Figure 3: The Preprocess tab of WEKA Explorer showing the composition of the imported dataset and some visualizations. Users can click the Visualize All button to become more familiarized with the dataset.  
In Figure 4, it can be seen that the red bar has been increased, indicating that there are additional samples added into the \"No\" class. Now that the minority class has gained its number, the dataset is still imbalanced. To address this, the researchers used the Spread Subsample to reduce the number in the majority class and make it even with the minority class. The updated numbers of the classes in the dataset can be seen in Figure 5.  
In Figure 5, it can be noted that the heights of the blue and red bars are now equal, indicating that both classes have the same values. This is the dataset that has been utilized in this study after implementing both the SMOTE and the Spread Subsample techniques. It is important to use a balanced dataset so that the classifier is well informed on both classes to be predicted, as well as to avoid distribution bias [25].