## 4 Results and Discussion  
The COVID-19 and influenza datasets were merged to form a unified dataset for implementing the models. As mentioned in Section 2, the numbers 0 and 1 indicated positive and negative cases of COVID-19, while 2 and 3 indicated positive and negative cases of influenza. The balanced RT-PCR datasets contained 916 instances for each class, the rapid test datasets contained 646 instances for each class, and both tests combined included 1564 instances for each class. The unbalanced RT-PCR datasets contained class 1 (1863), 2 (1502), 3 (1423), and 0 (916); the rapid test datasets contained class 1 (16,594), 2 (691), 0 (648), and 3 (646); and both tests combined included class 1 (18,457  
Figure 1: Average ROC curve for each class of the DT model using both balanced tests. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Database** & **Model** & **Precision** & **Recall** & **Accuracy** & **Brier Score** \\ \hline \multirow{8}{*}{PCR} & MLP & 82.44 (82.72) & 82.72 (82.26) & 82.37 (82.25) & 0.088 (0.088) \\  & GBM & 82.59 (82.48) & 82.92 (82.14) & 82.54 (82.14) & 0.08 (0.089) \\  & RF & 83.04 (82.96) & 83.12 (82.47) & 82.84 (82.47) & 0.085 (0.087) \\  & DT & 82.46 (82.37) & 82.60 (81.76) & 82.27 (81.76) & 0.088 (0.091) \\ \multirow{2}{*}{Imbalanced} & XGBoost & 82.73 (82.67) & 82.99 (82.27) & 82.65 (82.27) & 0.086 (0.088) \\  & KNN & 82.16 (82.35) & 82.22 (81.51) & 82.07 (81.61) & 0.089 (0.092) \\  & SVM & 82.23 (81.93) & 82.55 (81.57) & 82.27 (81.57) & 0.088 (0.092) \\  & LRR & 71.76 (71.65) & 70.13 (70.78) & 71.37 (70.78) & 0.143 (0.145) \\  & LR & 72.29 (72.29) & 70.85 (71.38) & 72.02 (71.38) & 0.139 (0.143) \\ \hline \multirow{8}{*}{Rapid} & MLP & 85.30 (81.40) & 79.41 (81.40) & 96.44 (81.40) & 0.171 (0.092) \\  & GBM & 85.61 (81.14) & 79.40 (81.15) & 96.46 (81.15) & 0.017 (0.094) \\ \multirow{2}{*}{Imbalanced} & RF & 86.38 (81.50) & 79.25 (81.56) & 96.53 (81.56) & 0.017 (0.092) \\  & DT & 85.42 (80.99) & 79.61 (80.93) & 96.53 (80.93) & 0.017 (0.095) \\ \multirow{2}{*}{(Balanced)} & XGBoost & 85.76 (80.89) & 79.32 (80.96) & 96.47 (80.96) & 0.017 (0.095) \\  & KNN & 87.81 (80.52) & 78.93 (79.90) & 96.56 (79.90) & 0.017 (0.100) \\  & SVM & 86.12 (80.97) & 79.11 (81.11) & 96.49 (81.11) & 0.017 (0.094) \\  & LRR & 73.32 (68.78) & 55.60 (68.98) & 93.24 (68.98) & 0.033 (0.155) \\  & LR & 72.42 (70.70) & 58.37 (70.24) & 93.50 (70.24) & 0.032 (0.148) \\ \hline \multirow{8}{*}{Both} & MLP & 85.93 (80.95) & 71.11 (80.47) & 90.73 (80.95) & 0.046 (0.097) \\  & GBM & 85.89 (81.14) & 71.16 (80.64) & 90.70 (80.64) & 0.046 (0.096) \\ \multirow{2}{*}{(Balanced)} & RF & 86.85 (81.50) & 71.63 (80.95) & 90.96 (80.95) & 0.045 (0.095) \\  & DT & 85.60 (80.74) & 71.66 (80.13) & 90.76 (80.13) & 0.046 (0.099) \\ imbalanced & XGBoost & 86.07 (81.28) & 71.39 (80.77) & 90.79 (80.77) & 0.046 (0.096) \\ \multirow{2}{*}{(Balanced)} & KNN & 82.23 (80.33) & 66.15 (79.58) & 88.98 (79.58) & 0.055 (0.102) \\  & SVM & 86.43 (80.54) & 70.95 (80.11) & 90.77 (80.11) & 0.046 (0.099) \\  & LRR & 68.87 (70.16) & 52.82 (70.10) & 85.54 (70.10) & 0.072 (0.149) \\  & LR & 71.16 (71.24) & 55.39 (71.22) & 86.29 (71.22) & 0.068 (0.143) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of the classification models.