# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Experimental Results and Analysis  
The model evaluation measures are obtained through 10-fold cross-validation with the specificity, recall, G, and F1 value. Table 4 presents the results measured by the FNN and CNN models before using oversampling methods. The performances of all models are poor. In SVD, most model evaluation metrics are lower than 0.6 because the number of class distribution in the sample is unbalanced. Figure 7 shows the confusion matrix produced by each deep learning model using feature parameters to classify pathological and normal voices in the initial imbalanced class dataset. As shown in Table 4 and Figure 7,  
Figure 5: MFCCs of the fifth and fifteenth normal voice samples oversampled using various oversampling methods. the recognition rate of classifiers is biased toward samples of the majority class composed of pathological voices, whereas the accuracy for samples of the minority class composed of normal voice is insufficient. Therefore, the model misleads the overall accuracy results. From Figures 7-10, 72.96% accuracy is shown for the combination of MFCCs and the FNN. Additionally, accuracies of 72.31% and 71.34% are obtained with a combination of LPCs and MFCCs using the FNN and CNN, respectively. The lowest performance of 66.94% is obtained using the LPCs and CNN, as shown in Figure 10a. Table 5 shows the model evaluation matrices obtained from each classifier after performing imbalanced handling using various oversampling algorithms. The results in terms of binary class confusion matrices are shown in Figures 8 and 9. Model evaluations of FNN and CNN classifiers described in Table 5 show that VPD systems with SMOTE have better specificity, recall, G value, and F1 value. Compared to various deep learning models and the feature parameters, the combination of the CNN and LPCs oversampled by the SMOTE has the highest accuracy (98.89%), and the best overall performance is achieved when evaluating the model metrics with the implementation of the SMOTE algorithm. The optimal classifier, such as the CNN with LPCs oversampled by the SMOTE, increases the recall, the specificity, the G value, and the F1 value to 1.0, 0.97, 0.98, and 0.99, respectively. These studies are conducted on an imbalanced dataset, with significant deviations ranging from 0.20 to 0.73 between the recall and specificity. Our proposed method, which combines the CNN and LPCs oversampled by the SMOTE, improves the recall and specificity from 0.01 to 0.73 and 0.00 to 0.28, respectively, compared to the performance of other conventional methods. Figure 6: LPCs of the fifth and fifteenth normal voice samples oversampled by various oversampling methods. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{FNN} \\ \hline MFCC & Recall & 0.43 \\  & Specificity & 0.88 \\  & G value & 0.62 \\  & F1 value & 0.51 \\  & Recall & 0.35 \\  & Specificity & 0.90 \\  & G value & 0.56 \\  & F1 value & 0.45 \\ \hline CNN & & \\ \hline MFCC & Recall & 0.44 \\  & Specificity & 0.87 \\  & G value & 0.62 \\  & F1 value & 0.51 \\  & LPC & Recall & 0.27 \\  & Specificity & 0.95 \\  & G value & 0.51 \\  & F1 value & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation measures obtained by each model on the class-imbalanced SVD. Figure 8: The confusion matrices obtained by the FNN on the class-balanced dataset under various conditions. Figure 7: The confusion matrices produced for the MFCCs and LPCs by the FNN and CNN on the initial class-balanced dataset. Figure 10: Performance comparison among various combinations of methods on the class-imbalanced and class-balanced dataset.