## 4 Results and Discussion  
### Model Evaluation  
We used statistical indicators to assess the prediction model and further described it using binary classification. \(P\) represents positive dataset values, and \(N\) represents negative dataset values. True positive (TP) and true negative (TN) indicate that the predicted values are consistent with actual values, while false positive (FP) and false negative (FN) indicate that the predicted values differed from actual values [43,44]. In this study, the four following indicators were used for assessment:  
Accuracy: The ratio of datasets correctly predicted by the classifier to the total number of datasets. \[\text{Accuracy}=\frac{\text{TP}+\text{FN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{9}\]  
Precision: The ratio of correctly predicted positives to the total number of predicted positives in the sample. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
Recall: The ratio of correctly predicted positives to the total number of positives in the sample. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
F1-score: A measure of the precision of an algorithm by simultaneously including precision and recall. \[\text{F1-score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{12}\]  
Based on the preceding descriptions, accuracy is an indicator that directly measures the overall identification results but cannot reflect the actual status of each category. However, in the training process, most of the datasets in the major categories (Labels 0 and 1) could learn more information and features than the minor categories (Labels 2 to 5). This issue may reduce the identification capacity of the minor categories despite the high overall accuracy in the model evaluation. Since some classes of data have significant quantitative imbalances, we used random oversampling, which involves randomly selecting examples from the minority class, with replacement, and adding them to the training dataset but not for the testing dataset (unseen dataset). In this study, naive random oversampling was used for balancing and augmenting data. To avoid overfitting, naive random oversampling was used to generate new samples for balancing and augmenting data from the minority classes (\(2,3\) and \(5\)), which is underrepresented. The classification model for voiding dysfunction patterns was developed by dividing the TCMG database into training and testing datasets. K-Fold cross-validation resulted in statistically significant results. In K-Fold cross-validation, the original sample is randomly divided into k equal-sized subsamples. Among the k subsamples, one subsample is reserved as validation data for testing the model, and the remaining k - 1 subsamples are used as training data. The cross-validation process is then repeated k times, and each of the k subsamples is used only once as validation data. The k results can then be averaged to produce a single estimate. In this study, we used 10 observations for k = 2. After the data were shuffled, a total of 5 models were trained and tested. Then, the validation results were averaged over the rounds to estimate the model's predictive performance. Estimating the performance of the model can be resolved using the three indicators (precision, recall, and F1 score), thus highlighting their importance. One of the representative CNN results is shown in Table 1. On this basis, all indicators were used for model assessment, with the results shown in Table 1. The testing results revealed that the model recall accuracy was 98.19% for the weighted average and 98.09% for the macro average. The precision, recall, and F1 score of Labels 3 and 5 exceeded 99%, suggesting a higher classifying ability than the other labels. The result of the K-Fold cross-validation is provided in Table 2. As shown, the average accuracy of three indicators as assessed through K-Fold cross-validation was between 97.98% and 98.25%, indicating that the CNN model's predictive performance is significant.