## 4 Results and Discussion
### Model Evaluation
Table 3 shows comparisons between the performance of three different types of neural networks, using the same data settings as Table 1, in which CNN and recurrent neural network (RNN) with LSTM architecture had more than 0.9 accuracy, while the simple three-layer hidden layer had a precision of more than 0.9. In this study, the vibration spectrum was used as the input. For a general ANN, due to the lack of convolutional layers, we first flattened each spectral signal into one dimension and input it to a pure full connection \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multicolumn{2}{c}{**Support Data**} \\  & & & & & & **(Length in** \\  & & & & & & **Seconds)** \\ \hline
0 & Normal & 18 & 0.9647 & 0.9932 & 0.9787 & 86.17 s \\
1 & Decrease & 38 & 0.9937 & 0.9876 & 0.9906 & 183.45 s \\
2 & Flattened & 4 & 0.9979 & 0.9844 & 0.9911 & 16.07 s \\
3 & Intermittent & 5 & 0.9228 & 0.9977 & 0.9588 & 29.15 s \\
4 & Saw-tooth & 9 & 0.9989 & 0.9248 & 0.9604 & 47.65 s \\
5 & Tall and peak & 2 & 1.0000 & 0.9974 & 0.9987 & 13.05 s \\ \hline  & Macro-average accuracy & & 0.9797 & 0.9809 & 0.9797 & \\  & Weighted-average accuracy & & 0.9826 & 0.9819 & 0.9819 & 375.54 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of urination patterns in data sets and evaluation indicators. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**K-Fold**} & \multicolumn{2}{c}{**Macro-Average Accuracy**} & \multicolumn{2}{c}{**Weighted-Average Accuracy**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 0.9797 & 0.9809 & 0.9797 & 0.9826 & 0.9819 & 0.9819 \\
2 & 0.9760 & 0.9798 & 0.9772 & 0.9805 & 0.9797 & 0.9797 \\
3 & 0.9783 & 0.9823 & 0.9797 & 0.9831 & 0.9822 & 0.9822 \\
4 & 0.9866 & 0.9711 & 0.9787 & 0.9806 & 0.9805 & 0.9805 \\
5 & 0.9897 & 0.9849 & 0.9872 & 0.9881 & 0.9880 & 0.9879 \\ \hline Average & 0.9821 & 0.9798 & 0.9805 & 0.9830 & 0.9825 & 0.9824 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of K-Fold cross-validation for the CNN model. layer with 512 neurons; then we added an output full connection layer with a Softmax activation function for classification. As a result, the ANN model achieved a precision of 0.6873. In the case of multi-dimensional approaches, the LSTM and ANN models are difficult to detect effectively the features displayed by the frequency because there is no convolution method to extract the important features of the MFCC spectrum. However, LSTM has a good comparison of before and after information detection for time series, so the accuracy still has a certain feasible level. However, compared to ANN's approach of only having a purely full connection layer, its accuracy is much lower. To validate the classifiable nature of the collected data, the AI model developed in this study was also subjected to data clustering through uniform manifold approximation and projection (UMAP) data visualization [45]. The testing results provided in Tables 1 and 2 and the results of the UMAP data visualization analysis show that this measure indeed enhances data classification. The vibration signals in the study were converted into MFCC spectrum features. The UMAP approach was used to convert these spectrum features onto an N-to-2D plane (where N was selected to be two in this case), and the data labels were expressed in colors to visualize the data so that the model's performance could be easily observed, as shown in Figure 6a,b. As a result, as shown in Figure 6a, each class feature is rough and overlapping before training. After training, each label of data can be distinctly categorized as shown in Figure 6b; it is observed that different clusters can be segmented, thus confirming the classifiable nature of the datasets.