## 3 Proposed Model
### Experiment Results and Discussion
Figure 8: Diagnosis results of each method on dataset4: (**a**) AKNN-DMGCN; (**b**) BAKNN-DMGCN; (**c**) AKNN-GCN; (**d**) BAKNN-GCN. Figure 7: Diagnosis results of each method on dataset1: (**a**) AKNN-DMGCN; (**b**) BAKNN-DMGCN; (**c**) AKNN-GCN; (**d**) BAKNN-GCN. Figure 11: Graph structure constructed by AKNN for dataset1. Figure 10: Graph structure constructed by BAKNN for dataset4. Figure 9: Graph structure constructed by BAKNN for dataset1. According to the empirical formula [24], the value of k was calculated to perform the AKNN graph construction. Figure 13 presents the results of testing on four datasets using BP neural network (BPNN), CNN, KNN-GCN, AKNN-GCN, KNN-DMGCN, and AKNN-DMGCN models. As illustrated in the figure, for the balanced dataset, dataset1, the accuracy of AKNN-DMGCN reaches 97.22%, which is the highest among all methods, demonstrating the high expressive ability of this method. As the dataset imbalance increases, the accuracy of each algorithm decreases. The accuracies of CNN and BPNN decrease the most, while the accuracy of AKNN-DMGCN decreases the least. This is because the traditional CNN and BPNN only pay attention to numerical information and ignore structural information; therefore, they cannot obtain the full information of the signal and rely on numerous labeled data. The GCN can comprehensively consider both the structural and numerical information. Therefore, it can still exhibit high performance in the case of imbalanced datasets and fewer training samples. For the most imbalanced dataset, dataset4, the accuracy of AKNN-DMGCN only decreases by 2.21%, and the accuracy reaches 95.01%, which is 6.52% higher than that of KNN-GCN. This is partly due to the limited ability of KNN to construct graphs, resulting in a certain degree of unused information. In addition, because the GCN pays the same attention to all neighbors in the process of node feature forward propagation, the model is unable to adaptively assign weights according to the importance of neighbor nodes and does not distinguish between neighbor nodes. This significantly affects the model's ability to learn representative and important features in the graph structure. In contrast, AKNN can more effectively mine the relationship between nodes to adaptively assign different k values to each node, filter noise information, and ensure that key information is not lost, thereby constructing a more informative graph structure. DMGCN not only introduces a multi-attention mechanism but also achieves the transformation from static attention to dynamic attention, which greatly enhances the model's expressive ability. The combination of the powerful graph construction ability of AKNN and the powerful expressive ability of  
Figure 12: Graph structure constructed by AKNN for dataset4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\gamma\)} & \multicolumn{5}{c}{**Diagnostic Accuracy (\%)**} \\ \cline{2-5}  & **Dataset1** & **Dataset2** & **Dataset3** & **Dataset4** \\ \hline
1 & 96.20\% & 94.50\% & 92.67\% & 91.81\% \\
1.5 & 96.73\% & 94.43\% & 93.19\% & 92.53\% \\
1.75 & 97.22\% & 95.56\% & 94.26\% & 95.01\% \\
2 & 96.68\% & 95.34\% & 92.37\% & 94.16\% \\
2.5 & 96.73\% & 91. DMGCN greatly improves the model accuracy. In addition, there is basically no difference in the running times of these models in our dataset, which indicates that the algorithmic complexity of the proposed models has not increased. Figure 14 presents box plots of the accuracy of the five methods on the four datasets. As illustrated in the figure, the maximum and minimum values and median and quartiles of AKNN-DMGCN are significantly higher than those of other methods. Figure 14: Box plots of the diagnosis results. Figure 13: Diagnosis results of different methods. To demonstrate the differences between various models more intuitively, the results of the four models--KNN-GCN, AKNN-GCN, KNN-DMGCN, and AKNN-DMGCN--were compared for the most balanced dataset, dataset1, and the most imbalanced dataset, dataset4.