# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Oversampling Method Comparison  
Figure 5 shows the fifth and fifteenth MFCC distributions of the normal voice samples obtained before and after using various oversampling methods, including the ADASYN, SMOTE, and Borderline-SMOTE on the SVD database. Figure 5a,b show the fifth and fifteenth MFCCs of the original samples, respectively. Overall, when comparing the oversampled waveform with the original waveform, the amplitude is slightly higher, and the samples are slightly tighter in the oversampled MFCCs. The different components are partially marked with circles. In Figure 5c, the samples oversampled by the SMOTE are observed in the second and third circles. This waveform is more densely oversampled than the Borderline-SMOTE waveform of the original sample, as shown in Figure 5a. In Figure 5e, it is possible to observe the aspects oversampled by ADASYN in the last circle. When oversampled with Borderline-SMOTE, the dense waveforms can be observed in the first circle of Figure 5g. When oversampling the fifth MFCCs extracted from the normal voices using the three methods discussed, the SMOTE best interpolates the samples while maintaining the characteristics of the original MFCC of the fifth waveform, and the fifth MFCC oversampled by ADASYN appears to be the most similar to the original MFCC of the fifth waveform. In the case of the fifteenth MFCCs, in Figure 5d, the samples oversampled by the SMOTE are clearly observed in the first and second circles. When oversampling the fifteenth MFCCs extracted from normal voices using the three methods mentioned, the SMOTE best interpolates the samples, allowing for tight waveforms. The fifteenth MFCC oversampled by Borderline-SMOTE appears to be most similar to the original MFCC of the fifteenth waveform.  
Figure 6 shows the fifth and fifteenth LPC distributions obtained for the normal voice samples before and after using various oversampling methods, including the ADASYN, SMOTE, and Borderline-SMOTE, on the SVD database. Figure 6a,b show the original LPCs of the fifth and fifteenth samples, respectively. As is true of the aspects observed in the MFCCs, when comparing the oversampled waveform with the original waveform, the amplitude is slightly higher, and the samples are slightly tighter in the oversampled LPCs. The different components are partially marked with circles. In the LPCs oversampled by Borderline-SMOTE in Figure 6g,h, significant differences are observed in all circles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Class**} & \multicolumn{2}{c}{**Prediction Results**} \\ \cline{2-3}  & **Positive Class** & **Negative Class** \\ \hline Positive class & TP & FN \\ Negative class & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: The confusion matrix.  
Similarly, based on the results of other oversampling methods, such as ADASYN and the SMOTE, prominent differences are observed in the various circles shown in Figure 6c-f. Therefore, among the three methods, Borderline-SMOTE best interpolates the samples while maintaining the characteristics of the original LPC waveform.  
The fifth and fifteenth percentiles of the MFCCs and LPCs are randomly selected to show the oversampling phenomenon visually. Overall, as shown in Figures 5 and 6, the samples oversampled for the LPC tend to be better interpolated than those of the MFCC. In both cases, the samples oversampled using the ADASYN method tend to be similar to the original waveform. Consequently, an equal number of normal and pathological voice samples are produced. Although the data lengths in (c) to (h) appear to match those in (a) and (b) in Figures 5 and 6, this is because the oversampled samples overlap at the same time and are plotted together. In reality, the normal voice data samples are as numerous as the pathological voice data samples.