## 4 Empirical Studies
### Experimental Data, Environment, and Categorical Measures  
1 Experimental data  
The data used in the experiments of this paper come from the Good Doctor Online medical platform ([[https://www.haodf.com/](https://www.haodf.com/)]([https://www.haodf.com/](https://www.haodf.com/)), accessed on 10 March 2025), with Xijing Hospital of Air Force Military Medical University as the background, using the Python language to write a web crawler program to obtain the patient consultation data and the corresponding department information in the website. Firstly, the link of each department is obtained, and then the program continues to go into each department to crawl the information of this page and mark the corresponding department. In this chapter of the experiment, a total of 106,692 samples of patient consultation data from 36 departments were crawled. The data are shown in Figure 7. It can be seen that the amount of data in each department category is balanced, at around 3000 samples, which will help the later experiments to be carried out smoothly. Figure 6: Schematic diagram of circular convolutional layer structure. Experimental environment  
The experimental hardware platform is based on the Windows 10 64-bit operating system with 16G of video memory, the algorithms were written in Python 3.10.14, and the deep learning framework we used is Torch version 2.2.2. Disaggregated Measurement Indicators  
Whether the prediction is correct or not is generally classified into four categories in classification problems, TP (True Positive--actually this category, predicting the result as this category), FP (False Positive--actually aliased, predicting this category), FN (False Negative--actually this category, predicting aliased), and TN (True Negative--actually aliased), as shown in the Table 1, which is a rating scale based on the example of a dichotomous problem. FN (False Negative--actually this category, predicted this category) and TN (True Negative--actually this category, predicted this category), as shown in the Table 1, are the rating scales for the binary classification problem, and the evaluation metrics presented next are calculated around these four types of data. 1. Accuracy is the number of correct predictions in the sample as a proportion of the total sample, as shown in the following equation: \[\text{accuracy}=\frac{TP+TN}{TP+FP+FN+TN}\] (7)
2. The precision rate, also known as the rate of checking accuracy, is the proportion of samples predicted to be such samples that are actually such samples, which is defined as shown in the following formula: \[precision=\frac{TP}{TP+FP}\] (8)
3. The recall rate, also known as the check rate, is the proportion of correctly predicted samples among all samples that are actually of this type, which is defined as shown in the following equation: \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Projected Results**} \\ \hline actual result & standard practice & counter-example \\ standard practice & TP (true example) & FN (false negative) \\ counter-example & FP (False Positive) & TN (true counterexample) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation table of binary classification model. Figure 7: Patient consultation data distribution map of each department. \[recall=\frac{TP}{TP+FN} \tag{9}\]
4. The F1 score is the reconciled average of precision and recall, which is commonly used to assess model stability, and it is defined as shown in the following equation: \[F1-score=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}}\] (10)
5. The ROC curve is obtained using the FPR as the horizontal coordinate and the TPR as the vertical coordinate.