## 4 Results  
### Performance of the Machine Learning Algorithms  
Table 6 shows the confusion matrices for all the classifiers used in the study, which were evaluated via 10-fold cross-validation (K = 10) and train/test splitting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Testing** \\ **Method** \\ \end{tabular} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**NB**} & \multicolumn{2}{c}{**AB**} & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**SVM**} \\ \hline \hline K-fold & & 0 & 1 & & 0 & 1 & & 0 & 1 & & 0 & 1 & & 0 & 1 & & 0 & 1 & & 0 & 1 \\ cross- & 0 & 25 & 13 & 0 & 25 & 13 & 0 & 25 & 13 & 0 & 22 & 16 & 0 & 20 & 18 & 0 & 18 & 20 & 0 & 19 & 19 \\ validation & 0 & 23 & 215 & 0 & 16 & 222 & 0 & 23 & 215 & 0 & 16 & 222 & 0 & 12 & 226 & 0 & 12 & 226 & 0 & 17 & 221 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Train/test \\ splitting \\ \end{tabular} } & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 & \multicolumn{2}{c}{0} & 1 \\  & 0 & 9 & 3 & 0 & 8 & 4 & 0 & 8 & 4 & 0 & 9 & 3 & 0 & 7 & 5 & 0 & 6 & 6 & 0 & 6 & 6 \\  & 1 & 2 & 42 & 1 & 1 & 43 & 1 & 2 & 42 & 1 & 2 & 42 & 1 & 1 & 43 & 1 & 1 & 43 & 1 & 1 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrices for classifiers.  
Table 7 displays the accuracy of all the classification algorithms used in the study, demonstrating that all of them exceeded 85%. Moreover, DT, NB, and KNN (K = 7) exhibited superior accuracy in the testing methodologies.  
Figure 4 shows that the ROC curve for the KNN model demonstrated the highest accuracy in the train/test split evaluation and the K-fold cross-validation (Table 7).  
The different colors in the ROC curve represent the classifier's performance on each fold during the cross-validation process (Figure 4). Each colored line corresponds to the ROC curve obtained when one-fold of the data was used as the test set, and the remaining folds were used for training. This visualization allows us to see the variability in the classifier's performance across different subsets of the data. The Area typically summarizes the overall performance Under the ROC Curve (AUC), which is displayed at the top of the plot (AUC = 0.9149 in this case).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classification Algorithms** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline DT (K-fold) & 86.96 & 0.8849 & 0.8696 & 0.8757 \\ DT (Split) & 91.07 & 0.9087 & 0.9107 & 0.9093 \\ KNN (K-fold) & 89.49 & 0.8986 & 0.8949 & 0.8966 \\ KNN (Split) & 91.07 & 0.9093 & 0.9107 & 0.9058 \\ RF (K-fold) & 86.96 & 0.8849 & 0.8696 & 0.8757 \\ RF (Split) & 89.29 & 0.8888 & 0.8929 & 0.8892 \\ NB (K-fold) & 88.41 & 0.8846 & 0.8841 & 0.8842 \\ NB (Split) & 91.07 & 0.9087 & 0.9107 & 0.9093 \\ AB (K-fold) & 89.13 & 0.8848 & 0.8913 & 0.8848 \\ AB (Split) & 89.29 & 0.8914 & 0.8929 & 0.8914 \\ LR (K-fold) & 88.41 & 0.8748 & 0.8841 & 0.8782 \\ LR (Split) & 87.50 & 0.8732 & 0.8750 & 0.8732 \\ SVM (K-fold) & 86.96 & 0.8667 & 0.8696 & 0.8681 \\ SVM (Split) & 87.50 & 0.8732 & 0.8750 & 0.8619 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance metrics of each classification algorithm (both K-fold cross-validation and training/testing approach).  
Figure 4: ROC curve for the KNN model.  
Figures 5a-d and 6a-d present the performance of all the classifiers based on four metrics, Precision, Recall, F-measure, and Accuracy, evaluated using 10-fold cross-validation (K = 10) and the train/test splitting method.  
Figure 5: Performance of classifiers in terms of 10-fold cross-validation.  
Figure 6: Performance of classifiers in terms of train/test splitting.