## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{2}{c}{**Test Dataset**} \\ \cline{2-5}  & \(P\) & \(N\) & \(P\) & \(N\) \\ \hline
1 & 42 (462) & 459 & 11 & 115 \\
2 & 38 (457) & 463 & 15 & 111 \\
3 & 40 (465) & 461 & 13 & 113 \\
4 & 43 (449) & 458 & 10 & 116 \\
5 & 44 (465) & 457 & 9 & 117 \\
6 & 44 (463) & 457 & 9 & 117 \\
7 & 40 (462) & 461 & 13 & 113 \\
8 & 43 (462) & 458 & 10 & 116 \\
9 & 44 (468) & 457 & 9 & 117 \\
10 & 39 (474) & 462 & 14 & 112 \\ \hline \end{tabular}
\end{table} 
Table 1: The size of randomly selected datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Dataset** & \(T_{P}\) & \(T_{N}\) & \(F_{P}\) & \(F_{N}\) & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & 6 & 110 & 5 & 5 & 0.92 & 0.55 & 0.55 & 0.55 \\
2 & 7 & 104 & 7 & 8 & 0.88 & 0.47 & 0.50 & 0.48 \\
3 & 9 & 106 & 7 & 4 & 0.91 & 0.69 & 0.56 & 0.62 \\
4 & 6 & 108 & 8 & 4 & 0.90 & 0.60 & 0.43 & 0.50 \\
5 & 7 & 107 & 10 & 2 & 0.90 & 0.78 & 0.41 & 0.54 \\
6 & 7 & 109 & 8 & 2 & 0.92 & 0.78 & 0.47 & 0.58 \\
7 & 8 & 103 & 10 & 5 & 0.88 & 0.62 & 0.44 & 0.52 \\
8 & 8 & 110 & 6 & 2 & 0.94 & 0.80 & 0.57 & 0.67 \\
9 & 7 & 104 & 13 & 2 & 0.88 & 0.78 & 0.35 & 0.48 \\
10 & 4 & 107 & 5 & 10 & 0.88 & 0.29 & 0.44 & 0.35 \\ \hline \multicolumn{7}{c}{mean} & 0.90 & 0.63 & 0.47 & 0.53 \\ \end{tabular}
\end{table} 
Table 4: Efficiency of GB classifier models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Dataset** & \(T_{P}\) & \(T_{N}\) & \(F_{P}\) & \(F_{N}\) & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & 6 & 112 & 3 & 5 & 0.94 & 0.55 & 0.67 & 0.60 \\
2 & 12 & 106 & 5 & 3 & 0.94 & 0.80 & 0.71 & 0.75 \\
3 & 6 & 108 & 5 & 7 & 0.91 & 0.46 & 0.55 & 0.50 \\
4 & 6 & 110 & 6 & 4 & 0.92 & 0.60 & 0.50 & 0.55 \\
6 & 6 & 109 & 8 & 3 & 0.91 & 0.67 & 0.43 & 0.52 \\
7 & 9 & 106 & 7 & 4 & 0.91 & 0.69 & 0.56 & 0.62 \\
8 & 7 & 112 & 4 & 3 & 0.94 & 0.70 & 0.64 & 0.67 \\
9 & 5 & 108 & 9 & 4 & 0.90 & 0.56 & 0.36 & 0.43 \\
10 & 7 & 106 & 6 & 7 & 0.90 & 0.50 & 0.54 & 0.52 \\ \hline \multicolumn{7}{c}{mean} & 0.92 & 0.62 & 0.54 & 0.57 \\ \end{tabular}
\end{table} 
Table 2: Efficiency of MLPC models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Dataset** & \(T_{P}\) & \(T_{N}\) & \(F_{P}\) & \(F_{N}\) & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & 7 & 110 & 5 & 4 & 0.93 & 0.64 & 0.58 & 0.61 \\
2 & 8 & 101 & 10 & 7 & 0.87 & 0.53 & 0.44 & 0.48 \\
3 & 8 & 101 & 12 & 5 & 0.87 & 0.62 & 0.40 & 0.48 \\
4 & 3 & 1 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Dataset** & \(T_{P}\) & \(T_{N}\) & \(F_{P}\) & \(F_{N}\) & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & 7 & 82 & 33 & 4 & 0.71 & 0.64 & 0.18 & 0.27 \\
2 & 12 & 87 & 24 & 3 & 0.79 & 0.80 & 0.33 & 0.47 \\
3 & 9 & 78 & 35 & 4 & 0.69 & 0.69 & 0.20 & 0.32 \\
4 & 5 & 96 & 20 & 5 & 0.80 & 0.50 & 0.20 & 0.29 \\
5 & 6 & 85 & 32 & 3 & 0.72 & 0.67 & 0.16 & 0.26 \\
6 & 8 & 86 & 31 & 1 & 0.75 & 0.89 & 0.21 & 0.33 \\
7 & 9 & 87 & 26 & 4 & 0.76 & 0.69 & 0.26 & 0.38 \\
8 & 7 & 100 & 16 & 3 & 0.85 & 0.70 & 0.30 & 0.42 \\
9 & 6 & 88 & 29 & 3 & 0.75 & 0.67 & 0.17 & 0.27 \\
10 & 7 & 99 & 13 & 7 & 0.84 & 0.50 & 0.35 & 0.41 \\ \hline  & mean & & & 0.77 & 0.67 & 0.24 & 0.34 \\ \hline \end{tabular}
\end{table} 
Table 6: Efficiency of GNB classifier models. Figure 3: ROC curves for models of selected machine learning algorithms: (**a**) MLP, (**b**) AdaBoost, (**c**) GB, (**d**) KNN, (**e**) GNB, (**f**) ensemble classifier.