## 3 Results
### Modelling
#### 3.2.1 Hyperparameter Optimization  
This study aims to compare supervised machine learning algorithms to determine which is the most appropriate algorithm to be used in developing a COVID-19 predictor; however, the optimal performance of an algorithm can be achieved if the best configuration has been utilized in the modeling process. Because of this, the researchers performed hyperparameter optimization to determine the values at which the algorithm will perform best using the COVID-19 Presence and Symptoms dataset. To evaluate the performance of the model, 10-fold cross validation and a batch size of 100 were used for all the experiments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Number** & **Technique Used** & \
\begin{tabular}{c} **No. of Instances** \\ **from the Yes Class** \\ \end{tabular} &
\begin{tabular}{c} **No. of Instances** \\ **from the No Class** \\ \end{tabular} \\ \hline
1 & - & 4383 & 1051 \\
2 & SMOTE & 4383 & 2102 \\
3 & Spread Subsample & 2102 & 2102 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the SMOTE and Spread Subsample techniques. For the J48 DT algorithm, the researchers used the confidence factor and Unpruned options in WEKA. The results are displayed in Table 4.  
The researchers used a common value for the minimum number of instances that can be found on each leaf, which is two instances. There were six experimental trainings performed for DT; for the first three trainings, the Unpruned parameter was set to true, and the confidence factors were 0.25, 0.50, and 0.75, respectively. In this experiment, the model's performance was consistent at 98.57%. The same values of confidence factor were used in the last three trainings with the Unpruned parameter set to False, and the model's performance reached 98.45% for the 0.25 confidence factor, and 98.55% for both 0.05 and 0.75. Thus, the researchers decided to set the Unpruned parameter to True, which means that there is no pruning performed. Since the model's recorded performances were the same, the researchers considered the confidence factor of 0.25, which is the default value set by WEKA. The next algorithm used was the RF algorithm; the researchers used 100 as the number of iterations, which determines the number of trees in the random forest. The maximum depth was set to zero, which means that there are no boundaries with the depth or splits of each tree in the forest. The researchers tuned the bag size of the RF algorithm and performed three trainings, and the results are displayed in Table 5.  
In Table 5, the bag size hyperparameter of RF was tuned to have the values of 100, 75, and 50. For the bag sizes of 100 and 75, the performance yielded the same result, 98.81%, while for the bag size of 50, the performance slightly dropped by 0.02%, and became 98.79%. The researchers considered using a bag size of 100, which is the default value set by WEKA. The next algorithm tested was the SVM and the researchers used the C value and Kernel hyperparameters. The results of hyperparameter optimization for the SVM algorithm are shown in Table 6.  
For SVM, the C value serves as a regularization parameter, which controls how often the classfier will avoid mistakes in classifying the training samples [44]. The C values used were 1, 2, and 3. The Kernels used were Poly Kernel, Normalized Poly Kernel, and Pearson VII function (PUK). There were nine trainings performed, and according to the results, a C value of 1 yielded the best performance for all the Kernels used. The PUK yielded the best accuracy among the Kernels used, at 98.81%, and since the performance of the model was the same for all the C values used, the researchers considered using a C value of 1, which is the default value set by WEKA.