## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Best Parameters** & **Accuracy (\%)** \\ \hline RF & \{‘bootstrap’: True, ‘criterion’: ‘gini’, \(n\_\)estimators’: 100 & 80.25 \\ SVM-Linear & [’C’:0.1, ‘gamma’:1, ‘kernel’: ’linear’] & 77.76 \\ SVM-RBF & [’C’:10, ‘gamma’:0.1, ‘kernel’: ’rbf’] & 85.19 \\ SVM-Poly & [’C’:10, ‘gamma’:0.1, ‘kernel’: ‘poly’], & 79.01 \\ ANN (3-6-6-1) & \{‘batch_size’: 32, ‘epochs’: 500, ‘optimizer’: ‘rmsprop’] & 86.42 \\ ANN (3-20-20-1) & \{‘batch_size’: 25, ‘epochs’: 500, ‘optimizer’: ‘adam’\}, & 85.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The accuracies achieved by the ML models after performing grid search with 10-Fold Cross-Validation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Best Parameters** & **Accuracy (\%)** \\ \hline RF & \{‘bootstrap’: True, ‘criterion’: ‘entropy’, ‘n,estimators’: 100 & 80.25 \\ SVM-Linear & [’C’:1, ‘gamma’: 1, ‘kernel’: ’linear’], & 79.01 \\ SVM-RBF & [’C’:1, ‘gamma’:1, ‘kernel’: ’rbf’] & 82.72 \\ SVM-Poly & [’C’:0.1, ‘gamma’:0.1, ‘kernel’: ’poly’] & 79.01 \\ ANN (3-6-6-1) & \{‘batch_size’: 25, ‘epochs’: 500, ‘optimizer’: ‘adam’\} & 85.19 \\ ANN (3-20-20-1) & \{‘batch_size’: 25, ‘epochs’: 100, ‘optimizer’: ‘adam’\}, & 83.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The accuracies achieved by the machine learning (ML) models after performing grid search with 5-Fold Cross-Validation. Based on the ML models, we performed two categories of experiments, (1) K-Fold Cross-Validation without Grid Search, and (2) Grid Search with K-Fold Cross-Validation. In each of the experiments, we performed both 5-Fold and 10-Fold Cross-Validation. When dealing with K-Fold Cross-Validation, 5 or 10 Fold is mostly preferred [30]. In terms of the 5-Fold Cross-Validation, without Grid Searching, the RF model achieved a mean accuracy of 79.01%. By evaluating the RF model based on the K-Fold Cross-Validation, without grid searching, the baseline model, LR outperformed the RF model as seen from Table 17.  
Among the SVM classifiers, The SVM-Linear achieved a mean accuracy of 79.25% based on the 5-Fold Cross-Validation as shown in Table 17. We can observe a decrease in accuracy from 79.25% to 77.24% after the 10-Fold CV as seen from Table 17. Also, based on the K-Fold Cross-Validation, without Grid Searching, the baseline model, LR outperformed the SVM-Linear model as seen from Table 17. The SVM-RBF classifier achieved a mean accuracy of 82.62% based on the 5-Fold Cross-Validation as seen from Table 17. With the 10-Fold Cross-Validation, the SVM-RBF classifier achieved a mean accuracy of 78.87% as seen from Table 17. We can observe a decrease in accuracy from 82.62% to 78.87% after the 10-Fold CV by the SVM-RBF classifier as shown in Table 17. By comparing the SVM-RBF with the LR model, based on the 5-Fold Cross-Validation, the SVM-RBF model outperformed the LR model. Whereas, based on the 10-Fold Cross-Validation, the LR model slightly outperformed the SVM-RBF model as seen from Table 17. Finally, the SVM-Poly classifier achieved a mean accuracy of 76.42% based on the 5-Fold Cross-Validation as seen from Table 17. With the 10-Fold Cross-Validation, the SVM-Poly classifier achieved a mean accuracy of 76.96%. We can observe an increase in accuracy from 76.42% to 76.96% after the 10-Fold CV by the SVM-Poly classifier as seen from Table 17.0 By comparing the SVM-Poly model with the LR model, based on the K-Fold Cross-Validation, without grid search, the LR model outperformed the SVM-Poly model.