## 4 Results and Discussion
The closer the AUC values of the models are to 1, then the more distinct their discriminatory power in distinguishing the classes is. In this regard, when the AUC values derived from the ROC curves, which are presented in Figure 5, were compared with each another, it was observed that all of the models, except for the SVM-based model, display a very good performance. With an AUC value of 0.997, the proposed model demonstrated an outstanding performance in terms of class discrimination. The DF and BDT-based models, with AUC values of 0.990 and 0.992, respectively, were the most similar to our model in terms of performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Confusion Matrix Parameters**} & \multicolumn{4}{c}{**Error Rates**} \\ \cline{2-7}  & **TP** & **FP** & **TN** & **FN** & **MCR\%** & **FPR\%** & **FNR\%** \\ \hline SVM & 374 & 25 & 375 & 26 & 6.38 & 6.25 & 6.50 \\ \hline LR & 375 & 12 & 388 & 25 & 4.63 & 3.00 & 6.25 \\ \hline BPM & 376 & 16 & 384 & 24 & 5.00 & 4.00 & 6.00 \\ \hline BDT & 382 & 13 & 387 & 18 & 3.88 & 3.25 & 4.50 \\ \hline DF & 386 & 17 & 383 & 14 & 3.88 & 4.25 & 3.50 \\ \hline NN & 396 & 81 & 319 & 4 & 10.63 & 20.25 & 1.00 \\ \hline AdaBoost & 381 & 20 & 380 & 19 & 4.88 & 5.00 & 4.75 \\ \hline Bagging & 379 & 22 & 378 & 21 & 5.38 & 5.50 & 5.25 \\ \hline RF & 387 & 17 & 383 & 13 & 3.75 & 4.25 & 3.25 \\ \hline FL-BDE & 396 & 11 & 389 & 4 & 1.88 & 2.75 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix parameters and error rates of FL-BDE, ML- and EL-based models at the split ratio of 0.60. Figure 4: Performance results of FL-BDE, ML- and EL-based models at the split ratio of 0.60. Figure 5: ROC curves of FL-BDE, ML- and EL-based models at the split ratio of 0.60. Table 4 compares the confusion matrix parameters and error rates of the FL-BDE, ML- and EL-based models at the split ratio of 0.70. As in the 0.60 split conditions, it can be clearly seen from the data in Table 4 that the best performance is reflected in the lowest MCR, FPR and FNR error rates under these split conditions for the proposed model. The proposed model, with an MCR value of 0.67% and an FPR value of 1.33%, achieved error rates that were 3% and 1% lower for both of the performance values, respectively, than those of the BPM-based model, which produced the closest result to it. Furthermore, it correctly classified all of the positive instances, resulting in an FNR of 0%. In terms of the MCR and FPR error rates, the NN-based model had the worst performance, with values of 9.83% and 18.67%, respectively. However, this model was the model that provided the best performance at this error rate, after the proposed model, with an FNR value of 1%. With an error value of 6.67%, the Bagging-based model had the worst performance in terms of the FNR. The performance results of the FL-BDE, ML- and EL-based models are shown in Figure 6 for the split ratio of 0.70. When these results were compared with the results obtained from the split conditions of 0.60, it was seen that all of the models, aside from the LR, DF, Bagging and RF based-models, improved their performances in terms of the most of the metrics, with the growth occurring in the number of instances trained at the split ratio of 0.70. The proposed model had the best performance in this period, improving its performance by 1.2% for accuracy, 1% for recall, 1.42% for specificity, 1.38% for precision and 1.2% for the F-measure, according to the performance results at the split conditions of 0.60. From the performance results at the split ratio of 0.70, it can be clearly seen that the FL-BDE model had a much better performance than the ML- and EL-based models did, with an accuracy of 0.9933, a recall of 1.00, a specificity of 0.9867, a precision of 0.9868 and an F-measure of 0.9934.