## 2 Materials and Methods
### Comparative Analysis  
The researchers performed a comparative analysis of the performances of different supervised machine learning algorithms using 10-fold cross-validation testing, and the important criteria used in this phase are the following.  
1. Accuracy  
Accuracy is the measurement of all the correctly predicted instances over the total predictions made by the model [39], and each algorithm can perform differently with regards to the correctly classified instances. Accuracy computes the ratio of the correctly classified instances that are true positives (_TP_) and true negatives (_TN_) over the total number of predictions, including the _TP_ and _TN_ and incorrect predictions, namely, false positives (_FP_) and false negatives (_FN_). Accuracy can be calculated using the following formula:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
Moreover, major accuracy measures were included in the comparative analysis, such as precision, recall and F-Measure. Precision measures the accuracy of the predictions of _TP_ over all the predicted positives by dividing the _TP_ by the sum of _TP_ and _FP_. According to the given description, precision means how many of those classified as COVID-19-positive are actually COVID-19-positive [39], and it can be calculated using this formula:  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
Recall measures the accuracy of prediction of _TP_ over the actual positive instances in the dataset. Recall answers the question, of all the instances who are COVID-19-positive,how many were correctly predicted by the model [39]? Recall percentage can be obtained by diving the _TP_ by the sum of _TP_ and _FN_.  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
Since the precision and recall measure different things, the value of the F-Measure measures the harmony, the balance, of the two criteria. The F-Measure is measured using the following formula. Conversely, the F-Measure score will decline if one criteria is improved at the expense of the other [39], and it can be calculated using this formula:  
\[F-Measure=2\times\frac{precision\times recall}{precision+recall} \tag{11}\]  
2. Correctly and Incorrectly Classified Instances  
These values were also considered in the comparative analysis of the machine learning algorithms. The correctly classified instances result is the sum of _TP_ and _TN_ predictions; conversely, the incorrectly classified instances result is the sum of the _FP_ and _FN_ predictions of the model [40].  
3. Kappa Statistic  
The [PERSON]'s kappa statistic calculates the reliability of the results between two raters of the same thing; it is how closely the raters agree by chance. A zero score means that there is random or less agreement between the two raters, and the score may be less than zero, whereas a score of \(1\) indicates complete agreement [41]. It can be calculated using the following formula:  
\[K=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{12}\]  
where \(P_{o}\) is the probability of agreement and \(P_{e}\) is the probability of random agreement between the raters.  
4. Mean Absolute Error (_MAE_)  
To evaluate the performance of the model, _MAE_ is used to measure the amount of misclassifications or errors in the model's prediction [42]. _MAE_ is the average of all the absolute errors; it determines how close the predicted value is to the actual value in the dataset. _MAE_ can be obtained by the following formula:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|x_{i}-x| \tag{13}\]  
where \(n\) stands for the total number of errors, \(\Sigma\) is the summation symbol, \(x_{i}\) is the predicted value, \(x\) is the actual value, and the vertical bars represent the absolute value [42].  
5. Time taken to build the model  
The researchers also considered the time taken to build the model, which is expressed in seconds. This value displays the amount of time needed to train the model, which is necessary to find out which model will perform the fastest.