## 4 Results
### Performance Measures  
Various classification algorithms such as random forest, support vector machine, k-nearest neighbor, and naive Bayes have been used in the classification of ALL. To ensure the effectiveness of the proposed methodology, several performance metrics [14; 42] have been calculated to perform a fair comparison between the classifiers and the influence of using the grey wolf optimization algorithm as a feature selection algorithm. These performance metrics are illustrated in Table 3. The evaluated metrics include accuracy, precision, recall (the most important one), F1-score, and specificity [43], which is required for evaluating binary classification. Figure 11: Proposed framework for leukemia diagnosis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Monitor** & **Equation** & **Description** \\ \hline Precision & TP/(TP + FP) & The metric represents the number of correct positive classes. \\ \hline Sensitivity & TP/(TP + FN) & The metric represents the number of correct positive class classifications made out of all positive class classifications. \\ \hline Accuracy & (TP + TN)/(TP + TN + FP + FN) & The classifier’s ability to correctly classify the class label \\ \hline Error (ER) & 1-Accuracy & The proportion of incorrect classifications \\ \hline F1-score & 2 \(\times\) [(Precision *  Recall)/(Precision +  Recall)] & Metric for combining recall and precision into a single score that takes into account both properties \\ \hline Specificity & TN/(TN + FP) & The ability of correctly identify people without illness \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Equations for confusion matrices with descriptions. These evaluation metrics indicators require the following parameters to be calculated:  
1. **\"TP\"** (true positives): the number of infected cases expected to be infected. 2. **\"TN\"** (true negatives): the number of uninfected cases expected to be uninfected. 3. **\"FP\"** (false positives): the number of uninfected cases expected to be infected. 4. **\"FN\"** (false negatives): the number of infected cases expected to be uninfected. 5. **Note:** In the proposed methodology the patient represents a positive class (0 is benign, 1 is malignant). The classification results before using the grey wolf optimization algorithm with all 203 features are shown in Table  
Figure 12a shows that (93) images are correctly recognized, whereas (8) benign samples are recognized as malignant type (false positive). It also discloses that (536) malignant samples are rightly classified, whereas only (1) samples are improperly classified as benign type (false negative). Corresponding results are found for the rest of the confusion matrices b, c, and d.  
The classification results after using the grey wolf optimization algorithm are shown in Table 5 and Figure 13.  
Figure 13a shows that (100) images have been correctly classified, whereas (1) benign samples are recognized as malignant type (false positive). It also discloses that (536) malignant samples have been correctly classified, whereas only (1) samples are improperly classified as benign type (false negative). Corresponding results are found for the rest of  
Figure 13: Confusion matrices analyses of the proposed model for (**a**) random forest, (**b**) support vector machine, (**c**) k-nearest neighbor and (**d**) naïve Bayes classifiers after using grey wolf optimization algorithm.