## 3 Results
The results are shown in Figure 4. RF and kNN achieved 85% accuracy, but RF outperformed kNN with an 85% F-score. Figure 5 shows the results with features whose correlation is \(>\)0.25 (6 features). Moreover, RF outperformed other models that achieved 85% accuracy, 82% sensitivity, 88% specificity and 85% for F-score. Figure 6 shows the correlation coefficient between each feature and the target diagnosis in ascending order. For feature ranking and selection, mRMR is also used. Table 5 shows the results using 17 features. RF and kNN achieved 87% accuracy, but RF outperformed kNN with an 87% F-score. Table 6 shows the results using nine features selected with mRMR. Moreover, RF outperformed other models achieving 84% accuracy, 80% sensitivity, 88% specificity and 83% for F-score. All the system components are developed using the Python scikit-learn library. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.82 & 0.78 & 0.87 & 0.81 \\ \hline LR & 0.81 & 0.78 & 0.84 & 0.80 \\ \hline kNN & 0.77 & 0.66 & 0.88 & 0.74 \\ \hline NB & 0.63 & 0.39 & 0.86 & 0.51 \\ \hline DT & 0.69 & 0.66 & 0.72 & 0.68 \\ \hline RF & 0.82 & 0.84 & 0.80 & 0.83 \\ \hline Bagging & 0.82 & 0.80 & 0.84 & 0.82 \\ \hline AdaBoost & 0.78 & 0.80 & 0.75 & 0.78 \\ \hline NN & 0.69 & 0.67 & 0.71 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results with 70–30% train-test split. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.83 & 0.79 & 0.87 & 0.83 \\ \hline LR & 0.81 & 0.78 & 0.84 & 0.80 \\ \hline kNN & 0.79 & 0.68 & 0.89 & 0.76 \\ \hline NB & 0.63 & 0.38 & 0.87 & 0.51 \\ \hline DT & 0.68 & 0.67 & 0.70 & 0.68 \\ \hline RF & 0.84 & 0.86 & 0.83 & 0.84 \\ \hline Bagging & 0.83 & 0.81 & 0.86 & 0.83 \\ \hline AdaBoost & 0.74 & 0.76 & 0.71 & 0.74 \\ \hline NN & 0.68 & 0.68 & 0.68 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results with 75–25% train-test split. Figure 4: Results using 10 features whose correlation \(>\) 0.1. Figure 5: Results using 6 features whose correlation \(>\) 0.25. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.79 & 0.68 & 0.90 & 0.76 \\ \hline LR & 0.78 & 0.66 & 0.90 & 0.75 \\ \hline kNN & 0.87 & 0.78 & 0.96 & 0.86 \\ \hline NB & 0.62 & 0.36 & 0.88 & 0.49 \\ \hline DT & 0.74 & 0.68 & 0.80 & 0.72 \\ \hline RF & 0.87 & 0.86 & 0.88 & 0.87 \\ \hline Bagging & 0.79 & 0.66 & 0.92 & 0.76 \\ \hline AdaBoost & 0.75 & 0.74 & 0.76 & 0.75 \\ \hline NN & 0.69 & 0.58 & 0.80 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results using first 17 features ranked using mRMR. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.79 & 0.68 & 0.90 & 0.76 \\ \hline LR & 0.78 & 0.66 & 0.90 & 0.75 \\ \hline kNN & 0.81 & 0.76 & 0.86 & 0.80 \\ \hline NB & 0.75 & 0.54 & 0.96 & 0.68 \\ \hline DT & 0.65 & 0.60 & 0.71 & 0.63 \\ \hline RF & 0.84 & 0.80 & 0.88 & 0.83 \\ \hline Bagging & 0.82 & 0.72 & 0.92 & 0.80 \\ \hline AdaBoost & 0.78 & 0.72 & 0.84 & 0.77 \\ \hline NN & 0.73 & 0.68 & 0.78 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results using first 9 features ranked using mRMR. Figure 6: Correlation coefficient between feature and diagnosis.