## 4 Results
### Evaluation  
Tables 5-7 show the confusion matrices of the predicted results for each of the classifiers using a different feature selection technique. The measured accuracy, precision, recall, and area under the curve (AUC) of the classifiers for the constructed models are shown.  
Table 5 represents the performance of the classification technique based on different balancing techniques. Without any balancing technique, the LR technique showed the best result, with an accuracy of 83.16%. With SMOTE, the RF technique showed the best result, with an accuracy of 82.97%. With bootstrapping, the RF technique showed the best result, with an accuracy of 90.53%.  
Table 6 displays the performance of classifiers utilizing the Gain Ratio technique based on various balancing methods. Without any balancing technique, the J48 technique demonstrated the highest accuracy, at 83.68%. With SMOTE present, the RF and J48  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Balancing Technique** & **Classifier Technique** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **AUC** \\ \hline Without Balancing & SVM & 83.68 & 0.852 & 0.956 & 0.901 \\ \hline  & NB & 76.84 & 0.913 & 0.777 & 0.839 \\ \hline  & LR & 83.16 & 0.865 & 0.929 & 0.896 \\ \hline  & RF & 82.11 & 0.877 & 0.895 & 0.886 \\ \hline  & J48 & 81.84 & 0.870 & 0.902 & 0.886 \\ \hline SMOTE & SVM & 81.25 & 0.834 & 0.882 & 0.857 \\ \hline  & NB & 76.72 & 0.892 & 0.723 & 0.799 \\ \hline  & LR & 79.56 & 0.827 & 0.858 & 0.842 \\ \hline  & RF & 83.41 & 0.871 & 0.868 & 0.870 \\ \hline  & J48 & 83.41 & 0.848 & 0.902 & 0.874 \\ \hline Bootstrapping & SVM & 85.79 & 0.856 & 0.983 & 0.915 \\ \hline  & NB & 80.79 & 0.937 & 0.807 & 0.868 \\ \hline  & LR & 83.42 & 0.861 & 0.939 & 0.898 \\ \hline  & RF & 91.05 & 0.946 & 0.939 & 0.942 \\ \hline  & J48 & 87.11 & 0.902 & 0.936 & 0.919 \\ \hline \end{tabular}
\end{table}
Table 6: Performance of the classifiers using the Gain Ratio technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Balancing Technique** & **Classifier Technique** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **AUC** \\ \hline Without Balancing & SVM & 84.47 & 0.858 & 0.959 & 0.906 \\ \hline  & NB & 75.00 & 0.897 & 0.767 & 0.827 \\ \hline  & LR & 83.95 & 0.871 & 0.932 & 0.900 \\ \hline  & RF & 82.63 & 0.864 & 0.922 & 0.892 \\ \hline  & J48 & 81.58 & 0.867 & 0.902 & 0.884 \\ \hline SMOTE & SVM & 75.22 & 0.855 & 0.736 & 0.791 \\ \hline  & NB & 82.33 & 0.850 & 0.878 & 0.864 \\ \hline  & LR & 78.23 & 0.842 & 0.811 & 0.826 \\ \hline  & RF & 85.13 & 0.854 & 0.926 & 0.888 \\ \hline  & J48 & 79.74 & 0.822 & 0.872 & 0.846 \\ \hline Bootstrapping & SVM & 85.53 & 0.855 & 0.980 & 0.913 \\ \hline  & NB & 75.26 & 0.901 & 0.767 & 0.828 \\ \hline  & LR & 84.74 & 0.872 & 0.943 & 0.906 \\ \hline  & RF & 93.16 & 0.944 & 0.970 & 0.957 \\ \hline  & J48 & & 0.897 & 0.939 & 0.917 \\ \hline \end{tabular}
\end{table}
Table 7: Performance of the classifiers using the Relief technique.  
techniques produced the best results, with an accuracy of 83.41%. With bootstrapping, the RF method achieved the best result, with an accuracy of 91.05%.  
Table 7 represents the performance of the classification technique based on different balancing techniques. Without any balancing technique, the SVM technique showed the best result, with an accuracy of 84.47%. With SMOTE, the RF technique produced the best results, with an accuracy of 85.13%. With bootstrapping, the RF technique showed the best result, with an accuracy of 93.16%.