## 3 Experiment
### Experimental Results and Analysis
As can be seen from Tables 9 and 10 regarding the time consumption of the machine learning model of the SVD database, although the performance of the deep model obtained the best results, the training time was also significantly extended compared to that of the other two types of models. Therefore, in our opinion, the ensemble learning model with a sub-optimal performance is still a preferred choice for pathological voice detection and classification. Besides this, the overall performance of the machine learning model in the SVD database is lower than that in the MEEI database, which may be due to the higher complexity of the data in the SVD database. This is because there are multiple types of speech disorders in the same voice sample in the SVD database, resulting in classifiers that do not classify well. The data complexity of the two databases can be shown indirectly through the sample point distribution in Figure 4. Thus, in the result of the SVD database, the model based on deep learning is better than the other learning models, and MLP yields good results for recall (0.75), specificity (0.94), \(G\) value (0.84), and \(F1\) value (0.76), which are better than those of the other models. However, it is worth noting that compared with the result of non-sampling, the performance of the VPD system constructed by the FC-SMOTE algorithm was significantly improved. Similarly, the binary classification results of the 10-fold cross-validation before and after the FC-SMOTE treatment are shown in Tables 10 and 11, respectively. Tables 11 and 12 show that the ten machine learning models with the FC-SMOTE algorithm in the SVD database perform better than the non-sampling models. The model evaluation measures produced by CNN are all 0.90, which is a little higher than that of other models. Thus, it can be concluded that the proposed method can effectively improve the performance of the pathological voice diagnosis model. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **LR** & **NB** & **DT** & **SVM** & **KNN** & **RF** & **XGBoost** & **GBDT** & **MLP** & **CNN** \\ \hline Recall & 0.67 & 0.64 & 0.67 & 0.64 & 0.64 & 0.68 & 0.67 & 0.61 & 0.75 & 0.74 \\ Specificity & 0.92 & 0.89 & 0.93 & 0.88 & 0.88 & 0.93 & 0.89 & 0.87 & 0.94 & 0.91 \\ \(G\) value & 0.78 & 0.74 & 0.78 & 0.74 & 0.75 & 0.78 & 0.74 & 0.70 & 0.84 & 0.80 \\ \(F1\) value & 0.69 & 0.63 & 0.69 & 0.64 & 0.64 & 0.70 & 0.63 & 0.60 & 0.76 & 0.72 \\ Time(s) & 1.03 & 0.23 & 0.45 & 0.38 & 0.31 & 15.42 & 19.26 & 19.81 & 24.20 & 49.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation measures of each machine learning model in the multi-class balanced SVD dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **LR** & **NB** & **DT** & **SVM** & **KNN** & **RF** & **XGBoost** & **GBDT** & **MLP** & **CNN** \\ \hline Recall & 0.47 & 0.33 & 0.28 & 0.41 & 0.37 & 0.45 & 0.31 & 0.33 & 0.31 & 0.37 \\ Specificity & 0.82 & 0.79 & 0.75 & 0.79 & 0.80 & 0.79 & 0.78 & 0.80 & 0.78 & 0.84 \\ \(G\) value & 0.61 & 0.49 & 0.45 & 0.56 & 0.54 & 0.53 & 0.48 & 0.51 & 0.48 & 0.55 \\ \(F1\) value & 0.43 & 0.31 & 0.32 & 0.49 To further demonstrate the effectiveness of FC-SMOTE in solving the problem of a class-imbalanced database, a comparison is made with SMOTE and two typical SMOTE-based extension approaches: Borderline-SMOTE and ADASYN. Table 13 shows the performance of SMOTE, Borderline-SMOTE, ADASYN, and the proposed FC-SMOTE with the optimal classifier in binary and multi-class pathological voice classification in the MEEI and SVD databases. The results show that our proposed FC-SMOTE method outperforms the other three methods by 0.02-0.17 and 0.03-0.14 in terms of the model evaluation measure in the MEEI and SVD databases, respectively. Thus, it can be concluded that the proposed method outperforms SMOTE, Borderline-SMOTE, and ADASYN in class-imbalanced pathological voice detection and classification.