## 4 Discussion  
We considered the serious class-imbalance in the MEEI database, an international standardized database for pathological voice detection research. Researchers have ignored this problem in the past. Therefore, using Acc as the main model evaluation measure in most research on modeling VPD systems is misleading. This paper proposed a fuzzy clustering algorithm-based SMOTE oversampling technology to balance classes for a pathological voice database with a class-imbalance. The FC-SMOTE algorithm improves \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Case**} & \multirow{2}{*}{**SMOTE**} & \multicolumn{2}{c}{**Borderlin**} & \multirow{2}{*}{**ADASYN**} & \multicolumn{2}{c}{**The Proposed**} & \multicolumn{2}{c}{**Performance Improvement**} \\  & & & & -SMOTE & & & \multicolumn{2}{c}{**FC-SMOTE**} & \multicolumn{2}{c}{**by Proposed Work**} \\ \cline{3-8} \multirow{3}{*}{**MEEI**} & \multirow{3}{*}{Binary} & \multicolumn{5}{c}{**Model Evaluation Measure**} \\  & & & & **(Recall/Specificity/G Value/F1 Value)** & & & & \\ \hline \multirow{3}{*}{MEEI} & \multirow{2}{*}{Binary} & 0.97/0.97 & 0.94/0.95 & 0.96/0.97 & 1.00/1.00 & (0.03\(\sim\)0.06)/(0.03\(\sim\)0.05) \\  & & /0.97/0.97 & /0.94/0.94 & /0.96/0.96 & /0.99/1.00 & /(0.02\(\sim\)0.05)/(0.03\(\sim\)0.06) \\ \cline{2-8}  & \multirow{2}{*}{Multi-class} & 0.74/0.89 & 0.67/0.84 & 0.74/0.89 & 0.82/0.96 & (0.08\(\sim\)0.15)/(0.07\(\sim\)0.12) \\  & & /0.80/0.75 & /0.71/0.69 & /0.78/0.70 & /0.88/0.86 & (0.08\(\sim\)0.17)/(0.11\(\sim\)0.17) \\ \hline \multirow{3}{*}{SVD} & \multirow{2}{*}{Binary} & 0.86/0.87 & 0.84/0.85 & 0.85/0.82 & 0.90/0.90 & (0.04\(\sim\)0.06)/(0.03\(\sim\)0.08) \\  & & /0.87/0.87 & /0.84/0.84 & /0.84/0.83 & /0.90/0.90 & /(0.03\(\sim\)0.06)/(0.03\(\sim\)0.07) \\ \cline{2-8}  & \multirow{2}{*}{Multi-class} & 0.72/0.86 & 0.69/0.80 & 0.71/0.85 & 0.75/0.94 & (0.03\(\sim\)0.06)/(0.09\(\sim\)0.14) \\  & & /0.78/0.70 & /0.74/0.69 & /0.77/0.70 & /0.84/0.76 & (0.06\(\sim\)0.10)/(0.06\(\sim\)0.07) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance comparison between SMOTE, Borderline-SMOTE, ADASYN, and the proposed FC-SMOTE in the MEEI database. \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 & 0.83 & 0.75 & 0.77 & 0.85 & 0.82 & 0.84 & 0.82 & 0.83 & 0.84 & 0.85 \\ Specificity & 0.51 & 0.50 & 0.58 & 0.53 & 0.53 & 0.52 & 0.52 & 0.56 & 0.63 & 0.65 \\ \(G\) value & 0.65 & 0.61 & 0.67 & 0.67 & 0.66 & 0.67 & 0.66 & 0.68 & 0.72 & 0.73 \\ \(F1\) value & 0.67 & 0.62 & 0.66 & 0.66 & 0.65 & 0.70 & 0.70 & 0.71 & 0.70 & 0.75 \\ Time(s) & 1.10 & 0.24 & 1.61 & 1.11 & 0.63 & 17.97 & 14.67 & 20.59 & 73.29 & 99.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Evaluation measures of each machine learning model in the normal and pathological voice class-imbalanced 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 & 0.89 & 0.86 & 0.85 & 0.88 & 0.88 & 0.90 & 0.88 & 0.89 & 0.89 & 0.90 \\ Specificity & 0.89 & 0.86 & 0.85 & 0.87 & 0.88 & 0.90 & 0.86 & 0.89 & 0.89 & 0.90 \\ \(G\) value & 0.89 & 0.86 & 0.85 & 0.88 & 0.88 & 0.90 & 0.88 & 0.89 & 0.89 & 0.90 \\ \(F1\) value & 0.87 & 0.83 & 0.85 & 0.87 & 0.85 & 0.87 & 0.87 & 0.87 & 0.88 & 0.90 \\ Time(s) & 3.24 & 0.26 & 1.94 & 1.51 & 0.81 & 18.84 & 21.61 & 25.96 & 96.40 & 179.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation measures of each machine learning model in the normal and pathological voice class-balanced SVD dataset. the SMOTE technique, which avoids unnecessary noise and classifier underfitting and overcomes the imbalanced data distribution between and within classes. FC-SMOTE has shown a good performance in processing class-imbalanced datasets with a high overlap density, such as in pathological voice samples.