## 3 Results
### Comparison Results  
Table 1 presents the performance measures derived from other studies, which are used for comparison with the proposed model's performance to demonstrate its effectiveness. It also presents the accuracy results obtained using various approaches that incorporate machine learning models, as applied by different authors. \begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Accuracy** & **Recall** & **F1 Score** & **Precession** \\ \hline SVM & 0.9741 & 1 & 0.9777 & 0.9565 \\ \hline KNN & 0.9741 & 0.9782 & 0.9782 & 0.9782 \\ \hline RF & 0.9571 & 0.9565 & 0.9670 & 0.9777 \\ \hline ANN & 0.9857 & 1 & 0.9890 & 0.9782 \\ \hline LR & 0.9571 & 0.9574 & 0.9677 & 0.9782 \\ \hline Proposed & 0.9947 & 0.99 & 0.99 & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance parameters for different matrices. Figure 2: (**a**) The optimal ROC curve, and (**b**) the optimal confusion matrix. Table 1 and Figure 3 show a comparison between the methods discussed above. The proposed method exhibits enhanced performance parameters. 2. [PERSON] [23] used the LR, SVM, KNN, and Naive Bayes algorithms. The performance parameters regarding the WDBC dataset are presented in Table 2 and illustrated in Figure 4. The proposed model achieved higher accuracy and recall compared to LR, KNN, and Naive Bayes, while it exhibited the same value as SVM. 3. The performance parameters from the study by [PERSON] et al. [24] and from this work are shown in Table 3 and Figure 5. The proposed model exhibited greater accuracy, recall, and AUC compared to SVM, LR, and KNN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Accuracy** & **Recall** \\ \hline LR & 96.49 & 0.9565 \\ \hline SVM & 98.24 & 0.99 \\ \hline KNN & 97.20 & 0.9808 \\ \hline Naive Bayes & 94.74 & 0.9545 \\ \hline proposed & 0.9947 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance parameters for different matrices. Figure 4: The performance parameters for different models vs. those for the proposed model. Figure 3: Performance parameters for different matrices in comparison with the proposed approach. 5. Table 5 and Figure 7 show the performance parameters from the study presented by [PERSON] et al. (2018) and those from this work. The proposed model has the highest values for all the parameters. 6. We compared our work with the work of [PERSON] et al. (2018), who applied a SVM, DT, LR, RF, KNN, and an NN to the WDBC dataset. The accuracy, recall, and F1 score are all higher for the proposed model, as shown in Table 6 and Figure 8.  
7. The performance parameters from the study presented by [PERSON] and [PERSON] (2018) and those from this work are shown in Table 7 and Figures 9 and 10. The model exhibited greater accuracy, precision, recall, and F1 score values than the KNN, SVM, DT, Na \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline SVM & 94.73 & 0.857 & 0.947 & 0.900 \\ \hline DT & 94.20 & 0.881 & 0.949 & 0.914 \\ \hline LR & 95.08 & 0.857 & 0.973 & 0.911 \\ \hline RF & 96.66 & 0.929 & 1 & 0.963 \\ \hline KNN & 93.32 & 0.786 & 1 & 0.880 \\ \hline NN & 90.35 & 0.762 & 0.970 & 0.854 \\ \hline proposed & 99.47 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance parameters for different matrices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Precision** & **Recall** \\ \hline RF & 95.9 & 97.2 & 96.3 \\ \hline KNN & 95.9 & 96 & 98.5 \\ \hline SVM & 97.2 & 97.3 & 99.0 \\ \hline Naïve Bayes & 92.4 & 92.6 & 95.5 \\ \hline proposed & 99.4 & 99 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance parameters for different matrices. Figure 7: The performance parameters for different models vs. those for the proposed model. Figure 8: The performance parameters for different models vs. those for the proposed model.