## 4 Performance Evaluation
### Discussion  
Statistically significant difference was not found between RNN models, but the total number of parameters of the PD detection model using RNN-SH is about 1/4 of the model using LSTM and about 1/3 of the model using GRU when the number of units and layers are the same. This difference becomes even wider when the units increase or multiple layers are used. Hence, it is very effective in terms of memory efficiency and faster learning. When using RNN-SH, it is easy to take measures against overfitting because there are few parameters, slow learning progresses, and only weight parameters for the input.  
Regarding the comparison when tanh and relu are used in RNN-SH, taking the case of one layer and 256 units for example, the scalar value \(a\), which is the gate parameter, is about 1.007 for tanh and about 1.012 for relu, and they are very close with each other. This was also the case under other conditions in these experiments. Therefore, it is considered that the difference in the performance of tanh and relu is caused by the difference only in activation function. From Figure 12, RNN-SH (tanh) with 256 units and two layers oscillate violently, and the reason why it could not learn well comes from the vanishing gradient at the output due to tanh. On the other hand, RNN-SH (relu) with 256 units and two layers could be learned smoothly; however, the accuracy was lower than that of tanh. Here, Figure 13 shows the examples of confusion matrix in the first trial of the tanh and relu RNN-SH. From Figure 13, the FP rate of relu was higher than that of tanh, and TN rate of relu was lower than that of tanh, indicating that the relu model could not identify PD patients properly and could not be learned well. Relu has gathered attention as the activation function that contributes to the multi-layering of neural networks. However, relu is not symmetric with respect to the origin and has problems such as a dying relu problem [30]. In our experiments, we did not use optimal parameter initialization or normalization methods that optimize for relu. Thus, dying relu may have occurred and caused the accuracy to deteriorate. Since relu is the function that is important for multi-layering, it is necessary to analyze in the future what is needed to improve the performance when using the relu.  
Figure 12: Learning curves of loss for each RNN with 256 units and one or two layers. (**a1,a2**), (**b1,b2**), (**c1,c2**), and (**d1,d2**) represent the graphs of learning curves of RNN-SH (tanh), RNN-SH (relu), LSTM, and GRU, respectively. The number after the letters corresponds to the number of layers.  
In terms of PD detection using an RP, the accuracy is around 70%. This is because some of the voice data of healthy people had hoarse voices and uneven voice volume. In our experiments, the voice/a/ was used; however, it was difficult for even a healthy person to utter the voice/a/ at a constant volume for a long time. Therefore, from the results of our experiments, the voice/a/ was not sufficient for PD detection. In order to improve the accuracy of PD detection, it is necessary to consider what pronunciation is more suitable for the detection in the future. Additionally, in our experiment, the RP images were resized by bilinear interpolation in consideration of the memory usage. However, when the RP image is compressed, features different from the original features may appear or important features may disappear. Since it is possible that the accuracy had deteriorated due to resizing, it is necessary to further study the image resizing method and the architecture of the CNN in the future.