# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 4 Conclusions  
This paper recommends a VPD system combined with various imbalanced learning algorithms. By analyzing the distributions between and within the sample classes, various oversampling methods are applied to minority class samples. This study also proposes oversampled MFCCs and LPCs as the input parameters of the deep learning model.  
First, we perform empirical and numerical experiments on SVD. We evaluate and validate deep learning model sets using the resulting balanced class dataset as input and select a reasonable metric set as the model evaluation scale. The experimental results are evaluated using several model evaluation metrics such as recall, specificity, G, and F1, which are reasonable measures for imbalanced class learning. The experimental results suggest that the VPD system without the various oversampling algorithms has a very poor performance in classifying minority and majority classes and struggles to distinguish pathological voices from normal voices. The VPD system with the CNN and LPCs oversampled by the SMOTE is a useful method for building the deep learning model for pathological voice classification, which means that the VPD model can train minority classes better  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Feature** & **Database** & **Methodology** & **Accuracy** \\ \hline
[2] & MFCC & SVD & BPGAN and GAN & 87.60\% \\
[24] & MFCC & MEEI & FC-SMOTE and RF & 100\% \\  & MFCC & SVD & FC-SMOTE and CNN & 90\% \\
[22] &. & SVD & IFCM and CGAN & 95.15\% \\
[26] & Spectrogram & Spanish Parkinson’s Disease Dataset (SPDD) & Semi supervised GAN & 96.63\% \\ Proposed method & MFCC and LPC & SVD & LPC based on the SMOTE and CNN & 98.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis between related works tested on various datasets.  
and achieve improved performance. Vocal tract information of pathological and normal voices is successfully extracted through linear predictive analysis. Finally, the performances of oversampling algorithms such as the SMOTE, ADASYN, and Borderline-SMOTE are discussed though waveform analysis in Figures 5 and 6. In pathological voice detection, the SMOTE achieves better performance than traditional oversampling methods.  
In the future, our method may help clinicians diagnose pathological voices, solving problems related to insufficient medical resources and shortening the diagnosis time required for patients. In addition, the SMOTE can learn more pathological voices to form an effective VPD system for the classification of a wider range of class-imbalanced pathological voices encountered in multi-classification problems. In addition, we will undertake a study to classify disease types and grade, roughness, breathiness, asthenia, strain (GRBAS) scales of pathological voices.  
Conceptualization, J.-Y.L.; methodology, J.-Y.L.; software, J.-Y.L. and J.-N.L.; validation, J.-Y.L. and J.-N.L.; writing--original draft preparation, J.-Y.L. and J.-N.L.; writing--review and editing, J.-Y.L.; visualization, J.-Y.L.; funding acquisition, J.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2022R1H1A209259811). The sponsor had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.  
Not applicable.  
The authors declare no conflict of interest.