# COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
## 3 Results
### Training Results  
The training step for ANFIS is performed by employing three MF types, i.e., Triangular, Trapezoidal, and Gaussian as shown in Tables 3 and 4. Table 3 presents the training results of ANFIS for COVID-19 cases, and Table 4 presents the training results of ANFIS for the COVID-19 mortality rate in Hungary. The results have been compared using evaluation metrics of RMSE and MAPE. According to Table 3, Gaussian MF type with MF number 3, BP optimum method, and linear output MF provided the highest performance by the lowest value of RMSE. On the other hand, as is precise, scenario 1 provided the lowest RMSE in comparison with the scenario 2 for the selected MF. Therefore, it can be concluded that scenario 1 is suitable for modeling COVID-19 cases in contrast with scenario 2. According to Table 4, it can be claimed that Gaussian MF provided the lowest error and highest accuracy compared with other MF types for the prediction of mortality rate. Also it can be claimed that, for the selected MF type, scenario 2 provides the highest performance compared with scenario 1 for the prediction of mortality rate. \begin{table}
\begin{tabular}{c} \hline \hline
**Accuracy and Performance Index** \\ \hline _Determination coefficient_ = \(\sqrt{\frac{N\sum\limits_{X}\left(XY\right)-\sum\limits_{X}\left(Y\right)\sum \limits_{Y}\left(Y\right)}{\sqrt{\left|N\sum\limits_{X}\frac{\left(XY\right) -\sum\limits_{X}\left(X\right)^{2}}{\left|N\sum\limits_{Y}\frac{\left(XY\right) -\sum\limits_{Y}\frac  
Tables 5 and 6 present results for the prediction of COVID-19 cases and mortality rate, respectively by MLP-ICA. According to Table 5, MLP architecture with 10 neurons in the hidden layer provided the highest accuracy for the prediction of the COVID-19 cases in the presence of both scenarios. However, Scenario 2 provided higher accuracy than scenario 1, according to the lowest RMSE value. According to Table 6, neuron number 14 for scenario 1 and neuron number 18 for scenario 2 provided the highest accuracy in integrating by ICA method in comparison with other architectures. By comparing the evaluation criteria values, it can be concluded that scenario 1 is more suitable than scenario 2 for the prediction of mortality rate using MLP-ICA. Figure 4 presents the plot diagram for the selected models according to Tables 3-6. In these figures, the vertical axes represent the \"target values\". In fact, the actual values and the horizontal axis is the \"predicted values\" or in another word the output value of the model. In these plots, the dash-line is the 1:1 line. The distance of each point from the dash-line discusses the accuracy of the prediction. Such that, points on the dash-line have the minimum error and increase the determination coefficient, and points that have distance from the dash line increase the error and reduce the accuracy in accordance with its distance and reduce the determination coefficient. As is clear from Figure 5, ANN provides a higher determination coefficient (0.9963, 0.9963, 0.9987, and 0.9987, respectively for the prediction of COVID-19 cases and COVID-19 mortality rate in the presence of scenario 1 and scenario (see the Figure 5)) than ANFIS. Also, the ability of ANN in the prediction of the COVID-19 mortality rate is higher than that for the prediction of COVID-19 cases. However, ANFIS provides a little different behavior than ANN. Such that, in ANFIS, scenario 1 provides the highest determination coefficient for the prediction of COVID-19 cases but scenario 2