# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 3 Results
### Comparison with Other Methods
#### 3.2.2 Performance of Multi-Classification  
This section compares the average performance of DiffRS-net with that of seven other methods in the multi-classification of various breast cancer subtypes. Table 3 lists the performance achieved by each method in the multi-classification of breast cancer subtypes, and test confusion matrices are displayed in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Accuracy**} \\ \hline
**Breast Cancer** & **Ensemble\_RF** & **Ensemble\_EN** & **Concate\_RF** & **Concate\_EN** & **DIABLO** & **SMPSL** & **DeepMo** & **DiffRS-net** \\
**Subtype Datasets** & & & & & & & & \\ \hline Basal vs. Her2 & 0.864 & 0.909 & 0.864 & 0.909 & 0.863 & 0.909 & 0.928 & **0.952** \\ Basal vs. LumA & 0.944 & 0.944 & 0.958 & 0.958 & 0.951 & 0.944 & 0.937 & **0.984** \\ Basal vs. LumB & 0.951 & 0.975 & 0.976 & 0.902 & 0.951 & 0.878 & 0.971 & **0.983** \\ Her2 vs. LumA & 0.919 & 0.903 & 0.919 & 0.935 & 0.968 & 0.935 & 0.938 & 0.955 \\ Her2 vs. LumB & 0.871 & 0.774 & 0.871 & 0.806 & 0.871 & 0.774 & 0.887 & **0.909** \\ LumA vs. LumB & 0.840 & 0.778 & 0.840 & 0.852 & 0.827 & 0.802 & 0.802 & **0.894** \\ \hline \multicolumn{8}{c}{AUC} \\ \hline Basal vs. Her2 & 0.824 & 0.800 & 0.750 & 0.857 & 0.841 & 0.895 & 0.972 & **0.983** \\ Basal vs. LumA & 0.895 & 0.895 & 0.915 & 0.925 & 0.917 & 0.963 & 0.951 & **0.996** \\ Basal vs. LumB & 0.941 & 0.954 & 0.964 & 0.920 & 0.933 & 0.881 & **0.996** & 0.991 \\ Her2 vs. LumB & 0.643 & 0.741 & 0.591 & 0.667 & 0.750 & 0.777 & **0.949** & 0.947 \\ Her2 vs. LumB & 0.667 & 0.625 & 0.778 & 0.699 & 0.714 & 0.644 & 0.912 & **0.939** \\ LumA vs. LumB & 0.778 & 0.673 & 0.790 & 0.803 & 0.753 & 0.718 & 0.893 & **0.939** \\ \hline \multicolumn{8}{c}{F1 Score} \\ \hline Basal vs. Her2 & 0.769 & 0.667 & 0.667 & 0.833 & 0.727 & 0.857 & 0.893 & **0.917** \\ Basal vs. LumA & 0.964 & 0.964 & 0.975 & 0.972 & 0.909 & 0.962 & 0.947 & **0.990** \\ Basal vs. LumB & 0.960 & 0.977 & 0.982 & 0.889 & 0.963 & 0.865 & 0.960 & **0.986** \\ Her2 vs. LumA & 0.957 & 0.955 & 0.957 & 0.966 & 0.983 & 0.964 & 0.973 & **0.976** \\ Her2 vs. LumB & 0.926 & 0.863 & 0.917 & 0.875 & 0.923 & 0.857 & 0.809 & **0.942** \\ LumA vs. LumB & 0.711 & 0.862 & 0.735 & 0.750 & 0.650 & 0.600 & 0.717 & **0.936** \\ \hline \end{tabular} The best results are in bold.  
\end{table}
Table 2: Comparison of the binary classification performance of breast cancer subtypes on multi-omics datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision-Macro** & **Recall-Macro** & **F1 Score-Macro** \\ \hline Concate\_RF & 0.750 & 0.820 & 0.643 & 0.721 \\ Concate\_EN & 0.837 & 0.846 & 0.769 & 0.806 \\ Ensemble\_RF & 0.731 & 0.654 & 0.590 & 0.621 \\ Ensemble\_EN & 0.827 & 0.672 & 0.587 & 0.627 \\ DIABLO & 0.769 & 0.696 & 0.604 & 0.647 \\ SMPSL & 0.865 & 0.876 & 0.795 & 0.833 \\ DeepMo & 0.856 & 0.712 & 0.767 & 0.738 \\ DiffRS-net & **0.913** & **0.932** & **0.829** & **0.878** \\ \hline \hline \end{tabular} The best results are in bold.  
\end{table}
Table 3: Comparison of the multi-classification performance of breast cancer subtypes on multi-omics datasets.  
From Table 3, it is evident that DiffRS-net surpasses other methods in all metrics. As illustrated in Figure 3, DiffRS-net achieves perfect classifications for Basal (error rate = 0). For LumA, which has the largest sample size, DiffRS-net secures the second-best classification (error rate = 5%). DiffRS-net performs the best in classifying LumB (error rate = 14%). For Her2, which has the smallest sample size, DiffRS-net's classification is less robust. This may be due to the insufficient sample size for Her2, resulting in the model not adequately learning the characteristics of this subtype. Nevertheless, DiffRS-net's strong performance in classifying other subtypes ensures its overall classification efficacy.