## 4 Results and Discussion
In addition, the AOBNN-BDNN methodology has identified normal class samples with \(accu_{y}\) of 100%, \(prec_{n}\) of 100%, \(reca_{l}\) of 100%, \(F_{score}\) of 100%, and MCC of 100%. Figure 5: Result analysis of AOBNN-BDNN approach under 70% of TR data. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Training Phase (70\%)** & & & & & \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **MCC** \\ \hline Benign & 98.53 & 99.68 & 97.80 & 98.73 & 97.02 \\ Malignant & 98.72 & 95.07 & 100.00 & 97.47 & 96.67 \\ Normal & 99.82 & 100.00 & 98.92 & 99.46 & 99.35 \\
**Average** & **99.02** & **98.25** & **98.91** & **98.55** & **97.68** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of the AOBNN-BDNN approach with various measures under 70% of TR data. The training accuracy (TA) and validation accuracy (VA) obtained by the AOBNN-BDNN methodology on the test dataset is established in Figure 7. The experimental result denoted the AOBNN-BDNN algorithm has reached higher values of TA and VA. In Particular, the VA is greater than TA. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Testing Phase (30\%)** & & & & & \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **MCC** \\ \hline Benign & 99.57 & 100.00 & 99.16 & 99.58 & 99.15 \\ Malignant & 99.57 & 98.68 & 100.00 & 99.34 & 99.03 \\ Normal & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\
**Average** & **99.72** & **99.56** & **99.72** & **99.64** & **99.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result analysis of the AOBNN-BDNN approach with various measures under 30% of TS data. Figure 6: Result analysis of AOBNN-BDNN approach under 30% of TS data. The training loss (TL) and validation loss (VL) obtained by the AOBNN-BDNN approach on the test dataset are displayed in Figure 8. The experimental outcome represented that the AOBNN-BDNN method has exhibited minimal values of TL and VL. To be specific, the VL is lesser than the TL. Figure 8: TL and VL analysis of AOBNN-BDNN methodology. Figure 7: TA and VA analysis of AOBNN-BDNN methodology. A clear precision-recall analysis of the AOBNN-BDNN technique on the test dataset is represented in Figure 9. The figure inferred the AOBNN-BDNN technique has resulted in enhanced values of precision-recall values in every class label. A brief ROC study of the AOBNN-BDNN method on the test dataset is shown in Figure 10. The outcomes signified that the AOBNN-BDNN methodology has displayed its capability in classifying distinct classes on the test dataset. Table 5 provides an overall comparative inspection of the AOBNN-BDNN method with recent approaches [11]. Figure 11 renders a brief study of the AOBNN-BDNN method with existing models in terms of \(prec_{n}\) and \(reca_{l}\). The experimental outcomes reported the betterment of the AOBNN-BDNN model. With respect to \(prec_{n}\), the AOBNN-BDNN model has attained increased \(prec_{n}\) of 99.56% whereas the SEODTL-BDC, ESD, LSVM, ESKNN, FKNN, and LD methods have obtained reduced \(prec_{n}\) of 99.18%, 98.89%, 98.41%, 97.55%, 96.93%, and 97.41% respectively. In addition, with regard to \(reca_{l}\), the AOBNN-BDNN method has achieved increased \(reca_{l}\) of 99.72% whereas the SEODTL-BDC, ESD, LSVM, ESKNN, FKNN, and LD algorithms have gained reduced \(reca_{l}\) of 98.18%, 98.11%, 97.87%, 98.27%, 97%, and 98.05% correspondingly. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F-Score** \\ \hline AOBNN-BDNN & 99.56 & 99.72 & 99.72 & 99.64 \\ SEODTL-BDC & 99.18 & 98.18 & 99.12 & 98.14 \\ ESD model & 98.89 & 98.11 & 99.07 & 98.88 \\ LSVM model & 98.41 & 97.87 & 98.73 & 98.29 \\ ESKNN model & 97.55 & 98.27 & 97.70 & 98.61 \\ FKNN algorithm & 96.93 & 97.00 & 97.42 & 96.93 \\ LD algorithm & 97.41 & 98.05 & 97.92 & 96.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of AOBNN-BDNN approach with existing methodologies. Figure 9: Precision-recall curve analysis of AOBNN-BDNN methodology.