# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 3 Results
### Ablation Study
#### 3.3.2 Multi-Classification  
This section discusses the average performance of different DiffRS-net models in the multiclass classification of breast cancer subtypes. As shown in Table 6, DiffRS-net outperforms other models across all metrics, particularly in regard to Accuracy and F1-score, achieving 0.913 and 0.878, respectively. The optimized DiffRS-net2 and DiffRS-net3 both demonstrate superior performance compared to DiffRS-net1, providing clear evidence of the effectiveness of both optimization techniques. Moreover, as can be found in Table 6, the superior performance of DiffRS-net over DiffRS-net4 and DiffRS-net5 further validates the enhanced capability of attention learning networks in the multi-classification of breast cancer subtypes. These results indicate that the combination of the differential analysis and multi-way association analysis can yield a more accurate multiclass classification of breast cancer subtypes.  
In Figure 4, we generate normalized confusion matrices to visualize the multi-classification performance of each DiffRS-net mode on each breast cancer subtype. As shown in Figure 4, DiffRS-net and DiffRS-net1 attained perfect accuracy in classifying the Basal subtype (error rate = 0). In the LumA subtype, which has the largest sample size, DiffRS-net's performance is slightly behind that of the best classifier by 3% (error rate = 5%). However, DiffRS-net achieves the optimal classification effectiveness in the LumB subtype (error rate = 14%). Overall, DiffRS-net surpasses all other variant models, conclusively demonstrating the definite efficacy of these two optimization techniques in the multi-classification task of breast cancer subtypes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**DiffRS-net Models** & **Accuracy** & **Precision-Macro** & **Recall-Macro** & **F1 Score-Macro** \\ \hline DiffRS-net1 & 0.827 & 0.806 & 0.733 & 0.768 \\ DiffRS-net2 & 0.846 & 0.868 & 0.764 & 0.813 \\ DiffRS-net3 & 0.865 & 0.892 & 0.696 & 0.782 \\ DiffRS-net4 & 0.856 & 0.893 & 0.736 & 0.807 \\ DiffRS-net5 & 0.865 & 0.898 & 0.719 & 0.799 \\ DiffRS-net & **0.913** & **0.932** & **0.829** & **0.878** \\ \hline \end{tabular}  
* The best results are marked in bold.  
\end{table}
Table 6: Performance of different DiffRS-net models on multi-classification for all subtypes of breast cancer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{5}{c}{**Accuracy**} \\ \hline
**Breast Cancer** & **DiffRS-Net1** & **DiffRS-Net2** & **DiffRS-Net3** & **DiffRS-Net4** & **DiffRS-Net5** & **DiffRS-Net** \\ \hline  & \multicolumn{5}{c}{F1 Score} \\ \hline Basal vs. Her2 & 0.831 & 0.848 & 0.857 & 0.728 & 0.770 & **0.917** \\ Basal vs. LumA & 0.966 & 0.973 & 0.971 & 0.971 & 0.967 & **0.990** \\ Basal vs. LumB & 0.951 & 0.962 & 0.979 & 0.937 & 0.941 & **0.986** \\ Her2 vs. LumA & 0.964 & 0.974 & 0.973 & 0.963 & 0.956 & **0.976** \\ Her2 vs. LumB & 0.923 & 0.931 & 0.934 & 0.922 & 0.894 & **0.942** \\ LumA vs. LumB & 0.672 & 0.707 & 0.699 & 0.672 & 0.582 & **0.936** \\ \hline \end{tabular}  
* The best results are in bold.  
\end{table}
Table 5: _Cont._