# COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
## 3 Results
### Training Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **No. of** & **No. of** & **No. of Initial** & **No. of** & & \\  & **Countries** & **Decades** & **Imperialists** & **Neurons** & **RMSE** & **MAPE** \\ \hline \multirow{3}{*}{Scenario 1} & 250 & 55 & 70 & 10 & 1.902 & 37.1 \\  & **250** & 55 & **70** & **14** & **1.9** & **36.99** \\  & 250 & 55 & 70 & 18 & 1.901 & 36.99 \\ \hline \multirow{3}{*}{Scenario 2} & 250 & 55 & 70 & 10 & 1.902 & 37.16 \\  & 250 & 55 & 70 & 14 & 1.917 & 37.18 \\ \cline{1-1}  & **250** & 55 & **70** & **18** & **1.902** & **37.09** \\ \hline \end{tabular}
\end{table} 
Table 6: MLP-ICA training results (mortality rate). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MF Type** & **No. of MFs** & **Optimum Method** & **Output MF** & **Epoch No.** & **RMSE** & **MAPE** \\ \hline \multirow{3}{*}{Scenario 1} & Triangular & 3 & Back-propagation & Linear & 204 & 31.14 & 47.12 \\  & Trapezoidal & 3 & Back-propagation & Linear & 135 & 31.09 & 44.36 \\  & _Gaussian_ & **3** & _Back-propagation_ & _Linear_ & **198** & **26.42** & **39.86** \\ \hline \multirow{3}{*}{Scenario 2} & Triangular & 3 & Back-propagation & Linear & 245 & 32.69 & 45.56 \\  & Trapezoidal & 3 & Back-propagation & Linear & 155 & 31.49 & 41.53 \\ \cline{1-1}  & _Gaussian_ & 3 & _Back-propagation_ & _Linear_ & **260** & **23.95** & **33.95** \\ \hline \end{tabular}
\end{table} 
Table 4: ANFIS training results (mortality rate). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **No. of** & **No. of** & **No. of Initial** & **No. of** & & \\  & **Countries** & **Decades** & **Imperialists** & **Neurons** & **RMSE** & **MAPE (\%)** \\ \hline \multirow{3}{*}{Scenario 1} & **300** & **40** & **50** & **10** & **37.30** & **23.15** \\  & 300 & 40 & 50 & 14 & 37.39 & 23.82 \\  & 300 & 40 & 50 & 18 & 37.48 & 23.57 \\ \hline \multirow{3}{*}{Scenario 2} & **300** & **40** & **50** & **10** & **37.24** & **23.47** \\  & 300 & 40 & 50 & 14 & 37.43 & 23.63 \\ \cline{1-1}  & 300 & 40 & 50 & 18 & 38.29 & 22.15 \\ \hline \end{tabular}
\end{table} 
Table 5: MLP-ICA training results (cases). provides the highest correlation coefficient for the prediction of mortality rate (0.9689, 0.934, 0.8909 and 0.9427, 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)). From considering Scen. 1 for mortality model using MLP-ICA method and Scen. 2 for mortality model using MLP-ICA method, the differences are negligible. It is, therefore, worth mentioning that using different scenarios for data sampling has a minimum effect on performance. Thus, using either odd or even days for data sampling gives relatively acceptable results. Furthermore, Figure 6 represents the deviation from target values for the COVID-19 cases from 4 March to 19 April. According to Figure 6, MLP-ICA scenario 2 provides a lower deviation from the target value followed by MLP-ICA in the presence of scenario 1 than the ANFIS in the presence of both scenarios. Figure 7 presents the deviation from target values for the COVID-19 mortality rate from 4 March to 19 April. According to Figure 7, MLP-ICA in the presence of scenario 1 provides a lower deviation from the target value followed by MLP-ICA in the presence of scenario 2 than the ANFIS in the presence of both situations. Figure 4: The initial empires generation [82]. Figure 5: \(Cont\). Figure 5: Plot Diagrams for the prediction of COVID-19 cases and mortality rate. By evaluating the modeling methods in the last sections, it was decided to select the MLP-ICA in the presence of scenario 2 for the prediction of COVID-19 outbreak and MLP-ICA in the presence of scenario 1 for the prediction of COVID-19 mortality rate in Hungary. Predictions were performed in two stages. The first stage for total prediction and the second one for daily prediction. Figures 8 and 9 present total cases and total mortality rate, respectively, and Figures 10 and 11 present the daily Prediction of the results from 20 April to 30 July.