## 4 Discussion  
The findings mentioned in the results section were carefully analyzed by the authors. The most appropriate model to use in building a COVID-19 predictor was determined by devising a summary of the machine algorithm's performance based on the results from Tables 9 and 10. The summary of the performances of the machine learning algorithms according to the criteria discussed in Section 2.5 is shown in Figures 8-10. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Correctly** & **Incorrectly** & \multirow{2}{*}{**Kappa**} & **Mean** & \multirow{2}{*}{**Time in**} \\  & **Classified** & **Classified** & & & **Absolute** & **Seconds** \\  & **Instances** & **Instances** & & & **Error** & \\ \hline
**J48 DT** & 4144 & 60 & 0.972 & 0.024 & 0.03 \\
**RF** & **4154** & **50** & **0.976** & 0.023 & 0.18 \\
**SVM** & **4154** & **50** & **0.976** & **0.012** & 3.12 \\
**k-NN** & 4149 & 55 & 0.973 & 0.022 & **0.01** \\
**NB** & 3951 & 253 & 0.880 & 0.080 & **0.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Supervised machine learning algorithms’ performance with [PERSON]’s COVID-19 Symptoms and Presence dataset using tuned hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline
**J48 DT** & 98.57\% & 0.986 & 0.986 & 0.986 \\
**RF** & **98.81\%** & **0.988** & **0.988** & **0.988** \\
**SVM** & **98.81\%** & **0.988** & **0.988** & **0.988** \\
**k-NN** & 98.69\% & 0.987 & 0.987 & 0.987 \\
**NB** & 93.98\% & 0.940 & 0.940 & 0.940 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Major accuracy measures from the 10-fold cross-validation performance of the models built using the supervised machine learning algorithms. Figure 8: This figure shows the barchart of the results of the major accuracy measures of the developed model using each algorithm. Figure 9: The bargraph that shows the number of correctly classified instances of the developed models. The blue bar represents the correctly classified instances and the red bar is the misclassified instances. In Figure 8, it can be seen that all the algorithms performed well in the training process, mainly because the hyperparameters had been tuned. However, there were slight differences in the accuracy, which is represented by a blue bar. The RF and SVM obtained the highest accuracy among all the algorithms, reaching a 98.81% score. Additionally, precision, recall and F-Measure were plotted in the bar chart using a red bar. The researchers used one bar for representing the three accuracy measures, for they all have equal values, and these values were converted to their corresponding percentages for visualization. The next most appropriate algorithm to use is the k-NN, with 98.69% accuracy, followed by J48 DT, which yielded a score of 98.57% accuracy, and lastly, the NB algorithm, which obtained 93.98% accuracy. The same ranking of the algorithms was obtained for the precision, recall, and F-Measure criteria, wherein RF and SVM shared first place with a 0.988 score, followed by KNN with 0.987, J48 DT with 0.986, and NB with 0.94. The correctly and incorrectly classified instances were also recorded and plotted using a barchart in Figure 9.  
Figure 9 shows that the algorithms RF and SVM achieved the same numbers of correctly and incorrectly classified instances. This result justifies the findings in Table 9 that the most accurate algorithms were RF and SVM, for these algorithms attained the most correctly classified instances, at 4154 instances out of the 4204 total instances in the dataset. Apparently, these algorithms achieved the lowest number of misclassified instances--50 instances. The next algorithm to be considered is k-NN, with 4149 correctly classified instances, and 55 misclassified instances, followed by J48 DT with 4144 correct and 60 incorrect classifications.