## 4 Performance Evaluation
### Input Vices and Preprocessing  
As mentioned in Section 3, the voice dataset used in the experiments are recordings of the voice/a/ for about 4 to 10 s. This dataset contains data for 22 healthy people (HP) and 30 PD patients, and three times data for each person is recorded. Therefore, the total number of data is 156 ((22 + 30) \(\times\) 3). The dataset contains 43 subjects (13 HP and 30 PD cases) who were hired as volunteers by the GYENNO SCIENCE Parkinson Disease Research Center (Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the \"Law of the People's Republic of China on Medical Practitioners\" (1998)  
Figure 5: The structure of [PERSON]’s disease detection model. The input image size (RP) is 20 \(\times\) 20, and the relu was used as the activation function after each output of the convolutional layer. In order to prevent overfitting, dropout is applied immediately after max pooling and before output layer. If the RNN has two layers, dropout is applied before the RNN of the second layer too. The size input to RNN is 64 \(\times\) 5 \(\times\) 5 (= 1600) because the output from the CNN is flattened. The model output is binary (2 classes), and the output is generated after all RPs have been input.  
declaration and its later amendments or comparable ethical standards), and nine subjects (nine HP) who were requested by the authors in order to alleviate the data imbalance. The breakdown of gender was 27 females and 25 males. PD patients consisted of patients with HY (Hoehn and Yahr) stage 1-5. A total of 13 PD patients were in stage 3-5, and 17 PD patients were lower than stage 3.  
Figure 6 shows the examples of preprocessed voice data and generated RPs in the procedure of Section 3. If the value of the 35 th percentile is \(p\) for the time series \(\left\{x_{i}\right\}_{i=1}^{N}=\left\{x_{1},\ x_{2},\ \cdots,\ x_{N}\right\}\), the recurrence plot matrix \(R\) is plotted by Equation (16) as follows:  
\[R(i,\ j)=\left\{\begin{array}{ll}1&\text{if}\ \big{|}x_{i}-x_{j}\big{|}<p\\ 0&\text{otherwise,}\end{array}\right. \tag{16}\]  
where \(i,\ j\in\left\{1,\ 2,\ \cdots,\ N\right\}\) are the numbers of the components. \(R(i,\ j)=1\) means that black is plotted, and \(R(i,\ j)=0\) means that white is plotted.  
We divided dataset into, train: 28 \(\times\) 3 (HP: 10 \(\times\) 3, PD: 18 \(\times\) 3, about 54%), validation: 12 \(\times\) 3 (HP: 6 \(\times\) 3, PD: 6 \(\times\) 3, about 23%), and test: 12 \(\times\) 3 (HP: 6 \(\times\) 3, PD: 6 \(\times\) 3, about 23%). However, the three data of the same person were divided so that they belonged to the same group (train or validation or test) in the experiments. Additionally, at the time of training, the voices of healthy person data shifted by 2 s were used as oversampling data so that the total number of data did not become imbalanced. Furthermore, in order to avoid the influence of dispersion due to data division, the data were shuffled, experiments were performed five times consecutively for each RNN, and the performance was evaluated based on the average.