# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FNN** & \multicolumn{2}{c}{**SMOTE**} & **ADASYN** & **Borderline-SMOTE** \\ \hline MFCC & Recall & 0.85 & 0.7 & 0.85 \\  & Specificity & 0.85 & 0.69 & 0.84 \\  & G value & 0.85 & 0.69 & 0.85 \\  & F1 value & 0.85 & 0.69 & 0.85 \\  & Recall & 0.92 & 0.92 & 0.91 \\  & Specificity & 0.91 & 0.91 & 0.9 \\  & F1 value & 0.91 & 0.91 & 0.9 \\ \hline CNN & & & & \\ \hline MFCC & Recall & 0.88 & 0.8 & 0.82 \\  & Specificity & 0.88 & 0.78 & 0.81 \\  & G value & 0.88 & 0.79 & 0.81 \\  & F1 value & 0.88 & 0.8 & 0.82 \\   LPC & Recall & **1.0** & 0.95 & 0.99 \\  & Specificity & **0.97** & 0.92 & 0.98 \\  & G value & **0.98** & 0.94 & 0.98 \\  & F1 value & **0.99** & 0.93 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation measures produced by each model on the class-balanced SVD. Figure 9: The confusion matrices produced by the CNN on the class-balanced dataset under various conditions. Figures 8 and 9 show the confusion matrices of each deep learning model using feature parameters to classify normal and pathological voices in the balanced dataset. Comparing the distributions of the confusion matrices in Figures 7-9, we find that the classification performance for majority class samples composed of pathological voices is lower for the VPD system using the FC-SMOTE algorithm, but it significantly improves its ability to classify minority class samples composed of normal voices. Figure 10b demonstrates that utilizing the CNN classifier with the LPCs oversampled by the SMOTE yields the highest accuracy at 98.89% compared to combinations of the other deep learning classifiers and feature parameters. Additionally, the next-highest performances (98.28% and 93.61%) are obtained via the LPCs oversampled by Borderline-SMOTE and ADASYN, respectively, with the CNN classifier. In the FNN classifier, the best accuracy (91.52%) is achieved with the LPCs oversampled by the SMOTE. The combination of the MFCCs oversampled by ADASYN and the FNN classifier yields the lowest performance of 69.53%. Overall, as a deep learning model, the CNN outperforms the single deep learning classifier for disordered voice detection, which has also been featured in the most recent published paper on this topic [6]. Additionally, the proposed method, such as the combination of the LPCs oversampled by the SMOTE and the CNN in the binary class confusion matrix of two models, is generally superior in terms of the exact prediction for each normal and pathological voice. The experimental results indicate that the SMOTE is a useful approach for building a binary classification model between pathological and normal voices. It also confirms that our suggested VPD algorithm can train minority classes better and can achieve an improved binary classification performance. Because the MFCC is widely used in speech signal processing, the VPD system can also achieve good classification and detection performances on imbalanced datasets, as demonstrated in a recent study [39; 40]. Therefore, the performances obtained from the MFCCs are better than those of the LPCs when using the two deep learning models in the class-imbalanced binary classification, as shown in Figure 10a. The performance of the FNN is approximately 6% better than that of the CNN. However, regarding the accuracies obtained on the balanced class dataset, all the models represent good predictive ability in the results of binary classification for normal and pathological voices, with LPCs oversampled by various methods. The deep learning model using the CNN achieves the best performance overall. Experimentally, the LPC appears to be more sensitive to oversampling methods than the MFCC. In conclusion, the CNN (as a deep learning model) and LPCs oversampled by the SMOTE as feature parameters obtain the highest performance in classification between pathological and normal voice using the SVD.