## 4 Results
### Performance Measures
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**NB**} \\ \cline{2-9}  & **Benign** & **Malignant** & **Benign** & **Malignant** & **Benign** & **Malignant** & **Benign** & **Malignant** \\ \hline
**Precision** & 99\% & 100\% & 96\% & 99\% & 96\% & 99\% & 80\% & 96\% \\ \hline
**AVG of precision** & 99.5\% & 97.5\% & 97.5\% & 88\% & 88\% & \\ \hline
**Recall** & 99\% & 100\% & 93\% & 99\% & 95\% & 99\% & 76\% & 96\% \\ \hline
**AVG of recall** & 99.5\% & 96\% & 97\% & 86\% & \\ \hline
**F1-score** & 99\% & 100\% & 94\% & 99\% & 96\% & 99\% & 78\% & 96\% \\ \hline
**AVG of F1-score** & 99.5\% & 96.5\% & 97.5\% & 87\% & \\ \hline
**Accuracy** & 99.69\% & 98.75\% & 98.59\% & 92.79\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance metrics for different classification techniques after using grey wolf optimization algorithm. the confusion matrices b, c, and d. Figure 14 shows the confusion matrices for all classes before and after using the grey wolf optimization algorithm. As shown in Figure 14, ALL detection before using the grey wolf optimization algorithm was able to achieve an average sensitivity of 96%, specificity of 92.1%, accuracy of 99%, F1-score of 97%, and precision of 99% when applying RF. In addition, it was able to achieve an average sensitivity of 89.5%, specificity of 80.2%, accuracy of 96%, F1-score of 92%, and precision of 94.5% when applying the SVM classifier. We were able to obtain an average sensitivity of 88.5%, specificity of 83.2%, accuracy of 95%, F1-score of 90%, and precision of 92% when applying the KNN classifier. When we applied the NB classifier, we reached an average sensitivity of 73%, specificity of 48.5%, the accuracy of 89%, F1-score of 76.5%, and precision of 82.5%. After using the grey wolf optimization algorithm as a feature-selection algorithm for enhancing the classification techniques, the accuracy of the classification techniques was improved, as was sensitivity, precision, F1-score, and specificity. For ALL detection after using the grey wolf optimization algorithm, it was able to achieve an average sensitivity of 99.5%, specificity of 99%, accuracy of 99.7%, F1-score of 99.5%, and precision of 99.5% when applying the RF classifier. In addition, it was able to achieve an average sensitivity of 96%, specificity of 93.1%, the accuracy of 98.8%, F1-score of 96.5%, and precision of 97.5% when applying the SVM classifier. We were able to obtain an average sensitivity of 97%, specificity of 95.1%, the accuracy of 98.6%, F1-score of 97.5%, and precision of 97.5% when applying the KNN classifier. When the NB classifier was applied, we achieved average sensitivity of 86%, specificity of 76.2%, the accuracy of 92.8%, F1-score of 87%, and precision of 88%. Figure 15 shows the effect of using grey wolf in feature selection on the confusion matrices. After using the grey wolf algorithm in features selection, it was found that the rate of increase in accuracy became 0.7% in RF, 2.8% in SVM, 3.6% in KNN, and 3.8% in NB. The rate of increase in precision became 0.5% after using RF, 3%, 5.5%, and 5.5% after using SVM, KNN, and NB, respectively. Sensitivity also increased after using the grey wolf technique and became 3.5%, 6.5%, 8.5%, and 13% of RF, SVM, KNN, and NB, respectively. The rate of increase in specificity became 6.9%, 12.9%, 11.9%, and 27.7% when using RF, SVM, KNN, and NB, respectively. Figure 14: Confusion matrices before and after using grey wolf optimization algorithm. The grey wolf optimization algorithm reduced the number of features while achieving the highest performance, which facilitates the process of detecting whether the blood sample is benign or malignant as shown in Figure 16.