## 3 Results
### Modelling
#### 3.2.1 Hyperparameter Optimization
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Number** & **Confidence Factor** & **Unpruned** & **Accuracy** \\ \hline
**1** & **0.25** & **True** & **98.57\%** \\
2 & 0.50 & True & 98.57\% \\
3 & 0.75 & True & 98.57\% \\
4 & 0.25 & False & 98.45\% \\
5 & 0.50 & False & 98.55\% \\
6 & 0.75 & False & 98.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: J48 DT algorithm hyperparameter optimization results using two as the minimum number of instances present in a leaf. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Number** & **Bag Size** & **Accuracy** \\ \hline
**1** & **100** & **98.81\%** \\
2 & 75 & 98.81\% \\
3 & 50 & 98.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RF algorithm hyperparameter optimization results using 100 as the number iterations or the trees in the forest, and a maximum depth of zero, which means unlimited depth. For the k-NN, the distance function used was Euclidean distance for all the trainings performed, and the researchers tuned the KNN paremeter (or the number of nearest neighbors used in the process), as well as the Cross-Validate parameter (whether a cross validation will be performed or not). The results of the training are presented in Table 7.  
In Table 7, the KNN values used were 1, 3, and 7, and for the Cross-Validate parameter, the values can be either true or false. The trainings that yielded high accuracy were the trainings that set the cross-validation parameter to true. The classifiers that performed cross-validation performed well, achieving 98.69% accuracy, and when the KNN value was 1, even the without cross-validation, the accuracy is still the same. The KNN values 3 and 7 without using cross-validation obtained lower accuracy, with 97.57% and 94.53%, respectively. Since the classifier performed equally well on the first three trainings performed, the researchers considered using the KNN value of 1 and the cross-validate parameter was set to true. Lastly, for the Naive Bayes algorithm, the Use Kernel Estimator and the Supervised Discretization parameters were used; the results are displayed in Table 8.  
In Table 8, it can be noticed that all the trainings using either Kernel Estimator or Supervised Discretization yielded the same results, 93.98%. In line with this, the researchers used the default value set by WEKA, which is the value of false for both parameters. For Tables 4-8, the rows in bold format indicate the configuration used in building the model for COVID-19 prediction that has undergone the comparative analysis, which is discussed in the following sections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Number** & **C** & **Kernel** & **Accuracy** \\ \hline
1 & 1 & Poly Kernel & 95.48\% \\
2 & 2 & Poly Kernel & 95.34\% \\
3 & 3 & Poly Kernel & 95.22\% \\
4 & 1 & Normalized Poly Kernel & 94.84\% \\
5 & 2 & Normalized Poly Kernel & 95.34\% \\
6 & 3 & Normalized Poly Kernel & 95.39\% \\
7 & **1** & **Pearson VII** & 98.81\% \\
8 & 2 & Pearson VII & 98.81\% \\
9 & 3 & Pearson VII & 98.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SVM algorithm hyperparameter optimization results using C value and Kernel hyperparameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Number** & **KNN** & **Cross Validate** & **Accuracy** \\ \hline
1 & **1** & **True** & **98.69\%** \\
2 & 3 & True & 98.69\% \\
3 & 7 & True & 98.69\% \\
4 & 1 & False & 98.69\% \\
5 & 3 & False & 97.57\% \\
6 & 7 & False & 94.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: k-NN algorithm hyperparameter optimization results using Euclidean as the distance function. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Number** & **Use Kernel** & **Supervised** & **Accuracy** \\  & **Estimator** & **Discretization** & **Accuracy** \\ \hline
**1** & **False** & **False** & **93.98\%** \\
2 & True & False & 93.98\% \\
3 & False & True & 93.98\% \\ \hline \hline \end{tabular}
\end{table}