## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **Top 1** & **Top 2** & **Top 3** & **Top 4** & **Top 5** \\ \hline \multirow{4}{*}{PCR} & MLP & Fever & Saturation & Respiratory distress & Gender & Cough \\  & GBM & Fever & Saturation & Respiratory distress & Gender & Cough \\  & RF & Fever & Saturation & Gender & Respiratory distress & Cough \\  & DT & Saturation & Fever & Gender & Cough & Respiratory distress \\  & XGBost & Saturation & Health professional & Respiratory distress & Fever & Fatigue \\  & SVM & Fever & Saturation & Respiratory distress & Gender & Cough \\ \hline \multirow{4}{*}{Rapid} & MLP & Fever & Saturation & Dysnoea & Respiratory distress & Gender \\  & GBM & Saturation & Dysnoea & Fever & Smell disorder & Respiratory distress \\  & RF & Saturation & Fever & Dysnoea & Fatigue & Gender \\  & DT & Fever & Saturation & Gender & Respiratory distress & Smell disorder \\  & XGBost & Saturation & Smell disorder & Rumny nose & Headache & Respiratory distress \\  & SVM & Fever & Saturation & Dysnoea & Respiratory distress & Gender \\ \hline \multirow{4}{*}{Both} & MLP & Fever & Saturation & Respiratory distress & Gender & Dysnoea \\  & GBM & Dharhea & Abdominal pain & Taste disorder & Headache & Runny nose \\  & RF & Abdominal pain & Diarrhea & Rumny nose & Headache & Health professional \\  & DT & Health professional & Abdominal pain & Headache & Taste disorder & Runny nose \\  & XGBost & Taste disorder & Cough & Gender & Dysnoea & Store throat \\  & SVM & Fever & Saturation & Respiratory distress & Gender & Dysnoea \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The five most relevant attributes for test prioritization when dealing with imbalanced datasets. Figure 2: Average precision-recall curve for each class of the DT model using both unbalanced tests. The null hypothesis and the results were as follows: for unbalanced RT-PCR (t = 217.363), balanced RT-PCR (t = 231.788), unbalanced rapid (t = 234.098), balanced rapid (t = 188.243), unbalanced both datasets (t = 221.810), and balanced both datasets (t = 253.074). The results suggest that the difference in mean recall was likely statistically significant (\(p<0.05\)). Additionally, depending on the dataset, MLP and GBM appeared to be statistically indistinguishable, as were DT, RF, XGBoost, KNN, and SVM. Using the permutation-based attribute importance method across the six datasets, we ranked the most significant five attributes for the ML models, demonstrating the highest performance. Fever and oxygen saturation symptoms displayed higher mean importance values in the case of the balanced datasets for RT-PCR and rapid tests. However, the symptoms with mean importance values appeared more diverse when both tests were balanced. As a result, the preprocessing of raw datasets facilitated the implementation, validation, and comparison of classification models with diverse characteristics, including using neural layers, tree ensembles, and data distance computation. This preprocessing also led  
Figure 3: **(A)** Mean recall for the models using unbalanced datasets for RT-PCR, rapid, and both types. **(B)** Mean recall for the models using balanced datasets for RT-PCR, rapid, and both types. to the public availability of patient data, including individuals tested as symptomatic using RT-PCR and rapid tests, as referenced in [8]. We conducted training and testing of the algorithms using both unbalanced and balanced datasets to improve data representativeness. When considering test-type grouping, the best classification metric results were achieved in both unbalanced and balanced scenarios for RT-PCR and rapid tests. While the classification model performances were similar for RT-PCR and rapid test scenarios, the RT-PCR testing scenario holds greater clinical relevance due to the high confidence associated with RT-PCR testing. The accuracy of RT-PCR testing enhances diagnostic confidence even if the patient has been tested in the early days after the onset of symptoms.