## 3 Results
DT model was selected as the best evolution model with 97.21% accuracy, and RF was selected as the second best model with 97.10% accuracy. For the multi-class dataset, the same ML model of the binary class was used. Table 3 shows the results of the original and preprocessing of the multi-class dataset. The original dataset contains 70 total patients and 206 features. The bagging and DT models show the best accuracy, with 84.51% and 84.31%, respectively. When the resampling method was used to overcome the problem of multi-class unbalanced data, the data reached a total of 203 patients, and 70 features were obtained using MI feature selection. 'RF' was selected as the best model, with 97.10% accuracy, and bagging as the second model, with 96.37%. RFE was used as a second feature selection in the preprocessing stage after MI. All ML models show good classification improvement by reducing the number of features to 15. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Processing** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline \multirow{3}{*}{RF} & Original & 91.42 & 81.25 & 81.26 & 81.37 & 75.69 \\  & MI & 97.10 & 94.70 & 99.10 & 97.29 & 94.34 \\  & MI-RFE & 97.10 & 94.77 & 99.12 & 97.29 & 94.44 \\ \hline \multirow{3}{*}{SVC} & Original & 88.57 & 78.57 & 68.75 & 73.33 & 66.10 \\  & MI & 86.41 & 88.52 & 99.12 & 93.71 & 87.0 \\  & MI-RFE & 93.80 & 91.22 & 99.14 & 93.99 & 88.0 \\ \hline \multirow{3}{*}{Bagging} & Original & 87.14 & 73.33 & 68.75 & 70.96 & 62.72 \\  & MI & 95.47 & 91.52 & 99.14 & 95.57 & 90.74 \\  & MI-RFE & 96.20 & 93.10 & 99.22 & 96.42 & 92.80 \\ \hline \multirow{3}{*}{DT} & Original & 87.14 & 76.92 & 62.5 & 68.96 & 60.96 \\  & MI & 95.30 & 91.52 & 99.20 & 95.55 & 90.60 \\ \cline{1-1}  & MI-RFE & 97.21 & 94.60 & 99.30 & 95.80 & 90.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the experimental method for a binary SCADI dataset using 10-fold cross-validation. RF and DT were selected as the best models for the multi-class dataset with 97.30% and 97.12%, respectively. All ML models for binary class I and multi-class II datasets were built using the default parameters as shown in Tables 1 and 2. Hyperparameters are used in ML algorithms to ensure the best results for the classification task. We employed grid-search to fine-tune the hyperparameters for every classification model for both datasets to obtain the best classification results (Table 4). As shown in Table 5, after the optimal hyperparameters were found, all the evaluation metrics of ML models show a good increase in performance where the RF model reached 99.10% classification accuracy in the binary dataset and 98.60% classification accuracy in the multi-class dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Model** & **Hyperparameters** \\ \hline \multirow{4}{*}{Binary} & RF & Number of trees = 90, Criterion = gini, \\  & SVC & C = 8, kernel = linear, gamma = 2 \\  & DT & Criterion = gini, max depth = 12, max\_features = 21 \\  & Bagging & Estimator = RF, number estimators = 50, \\  & & max samples = 0.9 \\ \hline \multirow{4}{*}{Multi} & RF & Number of trees = 150, Criterion = gini, \\  & SVC & C = 11, kernel = linear, gamma = 22 \\  & DT & Criterion = gini, max depth = 18, \\  & & max features = 12 \\  & Bagging & Estimator = RF, number estimators = 40, \\  & & max samples = 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter for SCADI datasets.