## 5 Conclusions  
This paper proposes a VPD system combined with an FC-SMOTE imbalanced learning algorithm. The FC-SMOTE combines the fuzzy c-means clustering algorithm with SMOTE. By analyzing the distribution between and within classes of the samples, different oversampling treatments are applied to minority class samples. The empirical experiment is firstly carried out in the MEEI database. A set of machine learning models are evaluated and validated using the resulting class-balanced dataset as an input, and a set of reasonable metrics is selected for model evaluation measures. Meanwhile, the effectiveness of FC-SMOTE is tested on an external validation set of the MEEI database. The experiment results are evaluated by the model evaluation measures recall, specificity, \(G\) value, \(F1\) value, and AUC/PRC, which are reasonable metrics for class-imbalanced learning. The experimental results show that the VPD system without the FC-SMOTE algorithm has a very poor performance in recognizing minority classes and can hardly multi-classify pathological voices types. The VPD system with FC-SMOTE is an effective method to build  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Features** & **Pathological** & **Normal** & **Types of** & **Classifier** & **Performance** \\  & **Extraction** & **Samples** & **Samples** & **Cross-Validation** & & **(Recall/Specificity/Acc)** \\ \hline
[45] & LLE +CD & 173 & 53 & 10 folds & GMM & 0.88/0.98/0.90 \\ \hline
[46] & MDVP & 95 & 53 & 10 folds & FDR & 0.45/0.93/0.76 \\ \hline
[47] & Entropy & 101 & 53 & 10 folds & SVM & 0.86/0.95/0.91\% \\ \hline
[48] & MFCC +Skeweness & 173 & 53 & 10 folds & GMM & 0.96/0.98/0.98 \\ \hline
[34] & MMTLS & 657 & 53 & 10 folds & SVM & 0.35/0.99/0.95 \\ \hline
[49] & MFCC & 118 & 36 & 4 folds & SVM & \(\sim\)/1.00/0.77 \\ \hline Proposed Method & MFCC & 136 & 136 & 10 folds & RF & 1.00/1.00/1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Results of the methods in the MEEI database (binary classification).  
a multi-classification model of pathological voice, which means that the VPD model can learn minority classes better to improve the VPD system's performance. Subsequently, to broaden the scope of the problem and maximize the applicability of the proposed method, the same experiment using the proposed method was conducted in the SVD database. The results again verify the effectiveness of our proposed method. Finally, the performance of FC-SMOTE and traditional oversampling algorithms are discussed. In pathological voice detection and classification, FC-SMOTE shows a better performance than traditional oversampling methods.  
In future research, our method can be applied to assist clinicians in diagnosing pathological voices, solving the problem of insufficient medical resources and shortening the diagnosis time for patients. Besides this, FC-SMOTE can further learn more pathological voice types to propose an effective VPD system for more types of class-imbalanced pathological voice multi-classification problems.  
Conceptualization, Z.F. and X.Z.; methodology, Z.F.; software, Z.F.; validation, Y.W., C.Z. and Z.T.; data curation, Y.W., C.Z.; writing--original draft preparation, Z.F.; writing--review and editing, Z.F.; visualization, Z.F; supervision, X.Z. and Z.T.; project administration, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 61271359.  
Not applicable.  
The MEEI database is commercialized and not publicly available. The SVD database, Available online: [[http://www.stimmdatenbank.coli.uni-saarland.de](http://www.stimmdatenbank.coli.uni-saarland.de)]([http://www.stimmdatenbank.coli.uni-saarland.de](http://www.stimmdatenbank.coli.uni-saarland.de)) (accessed on 12 April 2021).  
The authors declare no conflict of interest.