## 4 Discussion
Lastly, the NB had 3951 correctly classified instances and 253 incorrectly classified instances; this algorithm had the lowest number of correctly classified instances as well as the most misclassified instances, and these values indicate a large gap between the values of the other algorithms in this criteria. Based on the experimental results, RF and SVM are the machine learning algorithms that stand out among other supervised machine learning algorithms. RF and SVM were the top performers for most of the performance criteria. RF and SVM attained the highest accuracy, as well as the highest values in other accuracy measures, such as precision, recall and F-Measure. RF and SVM were also the best algorithms in terms of correctly classified instances, and had the least incorrectly classified instances. These findings mean that these algorithms using the optimized configurations were the most reliable algorithms in terms of identifying COVID-19 presence in a person based on the given symptoms. Moreover, the researchers used kappa statistics, MAE and time taken to build the model as criteria in the comparative analysis of the study. The combined charts for these measures are displayed in Figure 10.  
To evaluate the model's usefulness with respect to the true labels present in the dataset, the kappa statistic was used as a criteria. According to the results, the highest  
Figure 10: The barcharts for the kappa statistics (**a**), mean absolute error (**b**) and time taken to build the model (**c**). performing algorithms were still the RF and SVM, having a 0.976 kappa score. There is a close competition between J48 DT and k-NN, because J48 DT reached a 0.972 kappa statistic score, while KNN attained a score of 0.973. The lowest kappa score belongs to NB, at 0.880. In using [PERSON]'s kappa, the comparative analysis was made more reliable compared to merely using the accuracy. All the developed models displayed almost perfect interpretation agreement because all the scores were above 0.81, and this is close to 1. A value of 1 means perfect agreement between other classifiers. In this case, the first rater is the ground truth, or all of the actual values in the dataset, and the second rater used is the developed classifier. The authors considered using MAE to measure the error of the predicted classes as compared to the actual classes. In this way, the authors can determine how well the developed model will predict the labels of the samples as compared to the actual values present in the dataset. The authors considered the model with a low MAE to be more effective than ones with a higher MAE. The lowest MAE was attained by the SVM algorithm, with a 0.012 score; the lower the score, the lower the chance that the classifier will commit errors or misclassifications during the prediction. SVM was followed by k-NN, having a 0.022 MAE score. In the MAE criteria, SVM outweighed RF with a difference of 0.011, because RF attained a score of 0.023, which places RF in the third position under the MAE criteria, followed by J48 DT and NB, with 0.024 and 0.080 MAE scores, respectively. Lastly, the time taken to build the model was taken into consideration, since this is also provided as additional information after the 10-fold cross-validation in WEKA. Based on the results, the k-NN algorithm was the fastest algorithm to train a classifier model, followed by NB and J48 DT. Based on the results, the highest performers, RF and SVM, needed more time to train--RF took 0.18 s, and SVM took 3.12 s of training time. At the same time, these are the algorithms that performed the best. Altogether, the researchers found that the SVM algorithm with proper configuration or hyperparameter tuning is the most appropriate algorithm to be utilized in the development of a COVID-19 predictor. SVM attained 98.81% for the accuracy measure, and predicted 4154 instances of the 4204 total instances, misclassifying only 50. SVM also achieved a great score in the kappa statistics, at 0.976, which is the closest to the perfect agreement of 1.