## 4 Experiments Results
### Results
#### 4.4.2 The Performance Results of Models for Sub-AHS Dataset  
This section presents the performance results of the three approaches applied to the Sub-AHS dataset. The first approach is ML models; RF, DT, LR, and KNN were applied with TF-IDF and Unigram, bi-gram. The second approach is DL models; DeepCNN, CNN-LSTM, and CNN-GRU were applied with SkipGram and CBOW word embedding. The third approach is the proposed model. Table 5 shows the values of four metrics, including accuracy, precision, recall, and f1-score, of the testing results for the three approaches.  
For ML models, LR with unigram had the highest performance (85.96% for accuracy, 86.41% for precision, 85.96% for recall, and 85.13% for f1-score), while KNN with Bi-gram had 68.97% for accuracy, 47.56% for precision, 68.97% for recall, and 56.3% for f1-score). RF with unigram registered the second-highest result (83.74% for accuracy, 84.43% for precision, 83.74% for recall, and 82.47% for f1-score).  
For DL models, DeepCNN with CBOW achieved the highest performance (91.38% for accuracy, 91.31% for precision, 91.38% for recall, and 91.27% for f1-score), and it improved accuracy by 5.42%, precision by 4.9%, recall by 5.42%, and f1-score by 6.14% compared to LR with the unigram model. DeepCNN with SkipGram reached the lowest performance (89.41% for accuracy, 89.49% for precision, 89.41% for recall, and 89.44% for f1-score).  
We noticed that the classification performance of the proposed model with CBOW improved accuracy by 0.74%, precision by 0.85%, recall by 0.74%, and f1-score by 0.87% compared to CNN-LSTM with CBOW.  
Overall, we can see that the proposed model achieved the highest result compared to ML models and DL models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Feature Extraction Method**} & \multicolumn{4}{c}{**Testing Performance**} \\ \cline{3-6}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{ML approach} & \multirow{3}{*}{KNN} & Unigram & 79.83 & 82.32 & 79.83 & 76.31 \\ \cline{3-6}  & & Bi-gram & 70.89 & 50.26 & 70.89 & 58.82 \\ \cline{2-6}  & & Unigram & 83.0 & 82.86 & 83.0 & 82.92 \\ \cline{2-6}  & & Bi-gram & 80.12 & 80.04 & 80.12 & 77.97 \\ \cline{2-6}  & & Unigram & 87.9 & 88.29 & 87.9 & 87.19 \\ \cline{2-6}  & & Bi-gram & 82.71 & 82.69 & 82.71 & 81.33 \\ \cline{2-6}  & & Unigram & 80.69 & 84.15 & 80.69 & 77.24 \\ \cline{2-6}  & & Bi-gram & 83.29 & 83.14 & 83.29 & 82.13 \\ \cline{2-6}  & & Unigram & 86.17 & 86.96 & 86.17 & 85.06 \\ \cline{2-6}  & & Bi-gram & 81.56 & 82.1 & 81.56 & 79.5 \\ \hline \multirow{8}{*}{DL approach} & Deep CNN & \multirow{3}{*}{SkipGram} & 93.08 & 93.13 & 93.08 & 93.1 \\ \cline{2-6}  & CNN-LSTM & & 93.66 & 93.61 & 93.66 & 93.6 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 93.20 & 93.30 & 93.20 & 93.30 \\ \cline{1-1} \cline{2-6}  & Deep CNN & \multirow{3}{*}{CBOW} & 94.24 & 94.21 & 94.24 & 94.22 \\ \cline{1-1} \cline{2-6}  & CNN-LSTM & & 93.37 & 93.34 & 93.37 & 93.28 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 93.66 & 93.61 & 93.66 & 93.6 \\ \hline \multirow{2}{*}{The proposed ensemble model} & Stacking SVM & SkipGram & 94.95 & 94.9 & 94.95 & 94.9 \\ \cline{1-1} \cline{2-6}  & Stacking SVM & CBOW & 95.81 & 96.06 & 95.81 & 95.67 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance results of models for the Sub-AHS dataset.  
Additionally, Figure 6 presents ROC curve and AUC scores of ML, DL models, and the proposed model for the Sub-AHS dataset. The proposed model with CBOW and SkipGram word embedding and DEEPCNN achieved the highest AUC scores of 93. ML models with BI-gram have the lowest AUC scores of 50, 69.546, 73.507, 72.136 for KNN, DT, LR, and RF, respectively, compared to DL models and the proposed models.  
Overall, the proposed model with CBOW word embedding achieved the highest performance compared to ML models and DL models, and all evaluation metrics are consistent: accuracy, precision, recall, f1-score, and AUC.