## 4 Results and Discussion
It also produced a better performance in terms of the value differences, which are 3% or more for accuracy, 3.33% or more for recall, 1% or more for specificity, 1.08% or more for precision and 2.99% or more for the F-measure, than those of the RF, BPM and BDT-based models, which have the most similar performance results to it in terms of most of the confusion matrix metrics. The NN-based model had the worst performance in terms of all of the metrics, except for the recall metric. With a value of 0.99, it came the closest to that of the proposed model with regard to this metric. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Confusion Matrix Parameters**} & \multicolumn{4}{c}{**Error Rates**} \\ \cline{2-7}  & **TP** & **FP** & **TN** & **FN** & **MCR\%** & **FPR\%** & **FN\%** \\ \hline SVM & 283 & 18 & 282 & 17 & 5.83 & 6.00 & 5.67 \\ \hline LR & 281 & 10 & 290 & 19 & 4.83 & 3.33 & 6.33 \\ \hline BPM & 285 & 7 & 293 & 15 & 3.67 & 2.33 & 5.00 \\ \hline BDT & 290 & 12 & 288 & 10 & 3.67 & 4.00 & 3.33 \\ \hline DF & 287 & 14 & 286 & 13 & 4.50 & 4.67 & 4.33 \\ \hline NN & 297 & 56 & 244 & 3 & 9.83 & 18.67 & 1.00 \\ \hline AdaBoost & 282 & 9 & 291 & 18 & 4.50 & 3.00 & 6.00 \\ \hline Bagging & 280 & 15 & 285 & 20 & 5.83 & 5.00 & 6.67 \\ \hline RF & 286 & 9 & 291 & 14 & 3.83 & 3.00 & 4.67 \\ \hline FL-BDE & 300 & 4 & 296 & 0 & 0.67 & 1.33 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix parameters and error rates of FL-BDE, ML- and EL-based models at the split ratio of 0.70. Figure 7 shows the ROC curves and their AUC values for the models built using the proposed FL-BDE, ML- and EL-based methods at the split ratio of 0.70. When the AUC values of the models built here were compared with the AUC values at the split ratio of 0.60, it was seen that while there was no change in the performance result of the BDT model, the ones of the DF and NN-based models showed a decreasing tendency of 0.1. This decrease was by 0.3% in the Bagging model. On the other hand, the performance of the remaining models in terms of this metric improved by 0.2% or more. From the obtained results, it can be said that all of the models show good performances since they all obtained an AUC performance of 0.98 or more in the split ratio of 0.70. When the AUC results of the models were evaluated among themselves, the proposed model showed an excellent performance, with a very high value of 0.999. The closest AUC performance to this model was provided by the AdaBoost and RF-based models, with values of 0.993. Figure 8 shows the error rates related to the classification performance of the FL-BDE, ML- and EL-based models obtained using five-fold cross-validation. From the error rate values in the figure, it can be clearly understood that the FL-BDE model showed the best performance among all of the models, reaching the lowest error rate, with 1% and 0.1% values, respectively, in terms of both the MCR and the FPR. The proposed model achieved lower error rates of 2.1% in the MCR and 3% in the FPR than those of even the BDT model, which gave the most similar result to it. Although the NN model achieved the lowest error rate, with a value of 1.5 in terms of the FNR, it also has a high error rate value of 10.40% in terms of the MCR as a result of its very high FPR of 19%. This made it the best-performing model among all of the models. The LR was the model with the worst performance in terms of the FNR, with a value of 6.20%. When these results were compared with the results obtained in the conditions with the split ratios of 0.6 and 0.7 in terms of the MCR, it was seen that the error rate values of the Ensemble based-BDT, AdaBoost and Bagging models obtained by five-fold cross-validation were lower than the ones of the same models at both of the split ratios.