## 4 Results and Discussion

Table 2: Mean importance and SD for attributes in tree-based classification models using the imbalanced datasets. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Feature** & **MLP** & **SVM** \\ \hline \multirow{8}{*}{RT-PCR} & Respiratory distress & 0.139 (0.009) & 0.135 (0.008) \\  & Vomit & 0.041 (0.005) & 0.039 (0.004) \\  & Saturation & 0.146 (0.008) & 0.152 (0.007) \\  & Fatigue & 0.056 (0.005) & 0.059 (0.005) \\  & Diarrhea & 0.025 (0.003) & 0.028 (0.003) \\  & Abdominal pain & 0.015 (0.003) & 0.015 (0.003) \\  & Gender & 0.137 (0.007) & 0.125 (0.006) \\  & Health professional & 0.043 (0.003) & 0.021 (0.002) \\  & Fever & 0.230 (0.008) & 0.219 (0.008) \\  & Sore throat & 0.081 (0.007) & 0.069 (0.006) \\  & Dyspnoea & 0.100 (0.006) & 0.080 (0.006) \\  & Smell disorder & 0.013 (0.003) & 0.010 (0.002) \\  & Cough & 0.105 (0.007) & 0.098 (0.007) \\  & Runny nose & 0.010 (0.002) & 0.003 (0.002) \\  & Taste disorder & 0.018 (0.002) & 0.014 (0.002) \\  & Headache & 0.020 (0.002) & 0.007 (0.002) \\ \hline \multirow{8}{*}{Rapid} & Respiratory distress & 0.090 (0.007) & 0.092 (0.007) \\  & Vomit & 0.019 (0.004) & 0.019 (0.004) \\  & Saturation & 0.132 (0.007) & 0.125 (0.007) \\  & Fatigue & 0.067 (0.005) & 0.061 (0.006) \\  & Diarrhea & 0.018 (0.003) & 0.017 (0.004) \\  & Abdominal pain & 0.011 (0.003) & 0.007 (0.002) \\  & Gender & 0.090 (0.009) & 0.076 (0.007) \\  & Health professional & 0.017 (0.003) & 0.016 (0.003) \\  & Fever & 0.141 (0.007) & 0.143 (0.007) \\  & Sore throat & 0.064 (0.006) & 0.062 (0.006) \\  & Dyspnoea & 0.129 (0.008) & 0.118 (0.007) \\  & Smell disorder & 0.074 (0.005) & 0.075 (0.005) \\  & Cough & 0.071 (0.007) & 0.068 (0.008) \\  & Runny nose & 0.041 (0.003) & 0.047 (0.003) \\  & Taste disorder & 0.032 (0.004) & 0.034 (0.003) \\  & Headache & 0.049 (0.005) & 0.044 (0.004) \\ \hline \multirow{8}{*}{Both} & Respiratory distress & 0.123 (0.005) & 0.123 (0.005) \\  & Vomit & 0.038 (0.003) & 0.035 (0.004) \\  & Saturation & 0.146 (0.005) & 0.157 (0.006) \\  & Fatigue & 0.052 (0.003) & 0.047 (0.003) \\  & Diarrhea & 0.017 (0.002) & 0.016 (0.002) \\  & Abdominal pain & 0.020 (0.002) & 0.018 (0.002) \\  & Gender & 0.122 (0.005) & 0.109 (0.005) \\  & Health professional & 0.025 (0.002) & 0.018 (0.002) \\  & Fever & 0.180 (0.006) & 0.173 (0.006) \\  & Sore throat & 0.083 (0.004) & 0.083 (0.004) \\  & Dyspnoea & 0.108 (0.005) & 0.092 (0.005) \\  & Smell disorder & 0.058 (0.003) & 0.040 (0.002) \\  & Cough & 0.089 (0.005) & 0.079 (0.005) \\  & Runny nose & 0.023 (0.02) & 0.017 (0.001) \\  & Taste disorder & 0.030 (0.003) & 0.022 (0.003) \\  & Headache & 0.048 (0.003) & 0.028 (0.003) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean importance and SD for attributes in MLP and SVM models using the imbalanced datasets. The ROC and PR curves were computed using five random folds for each class. The tree-based models exhibited the highest Average Precision (AP) values, ranging from 58% to 86%, using the balanced dataset with both tests. Additionally, AP values ranged from 30% to 92% using the unbalanced dataset with both types of tests. Afterward, using the Friedman and Nemenyi tests increased confidence in validating the ML models. They were compared using the six COVID-19 and influenza datasets. The comparison predominantly concentrates on recall outcomes due to the significant adverse effects of false negatives in COVID-19 and influenza applications. Figure 3 depicts the recall results for the employed datasets.