## 3 Results
### Interpretability  
Before explaining the features, we need to understand that model interpretability is not always equal to causality. The SHAP values do not provide causality but provide insights into how the model behaves from data learning. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1 Score** & **ROC Area** & **PRC Area** & **Acc.** & **Category** \\ \hline MLP & 0.814 & 0.078 & 0.913 & 0.814 & 0.861 & 0.902 & 0.893 & 0.868 & non-SPSC \\  & 0.922 & 0.186 & 0.832 & 0.922 & 0.875 & 0.902 & 0.875 & SPSC \\ \hline C4.5 & 0.837 & 0.051 & 0.942 & 0.837 & 0.886 & 0.916 & 0.906 & 0.893 & non-SPSC \\  & 0.949 & 0.163 & 0.853 & 0.949 & 0.898 & 0.916 & 0.891 & 0.891 & SPSC \\ \hline AdaBoost & 0.846 & 0.051 & 0.944 & 0.846 & 0.892 & 0.931 & 0.916 & 0.898 & non-SPSC \\ C4.5 & 0.949 & 0.154 & 0.861 & 0.949 & 0.903 & 0.931 & 0.92 & 0.898 & SPSC \\ \hline Bagging & 0.842 & 0.051 & 0.942 & 0.842 & 0.889 & 0.927 & 0.915 & 0.895 & non-SPSC \\ C4.5 & 0.949 & 0.158 & 0.857 & 0.949 & 0.901 & 0.927 & 0.911 & 0.895 & SPSC \\ \hline Random & 0.853 & 0.048 & 0.947 & 0.853 & 0.897 & 0.939 & 0.932 & 0.902 & non-SPSC \\ Forest & 0.952 & 0.147 & 0.866 & 0.952 & 0.907 & 0.939 & 0.926 & 0.902 & SPSC \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the classification performance for different algorithms. Figure 3: Comparison of the ROC curves of (**a**) different algorithms and (**b**) random forest with different FN costs. First, we used a random forest to observe the importance of global features. The features arranged by the random forest method showed that age, stage, and gender were the top three impact features. The SHAP values in Figure 4 provide additional information. First, we agreed that age, stage, and gender demonstrate a critical and positive impact, which implies that larger feature values bring greater SPSC probability, whereas alcohol falls to the fifth rank. Moreover, the involvement of the regional lymph node ranks fourth. The red bars indicate the positive overall correlation coefficients. Next, we investigated the SHAP distribution of all the instances, as illustrated in Figure 6, i.e., using a bee swarm plot (Figure 5a) and heat map (Figure 5b). We observed how age contributes to the SHAP value per instance; however, we cannot investigate the correlation distribution. Therefore, we plotted the scattering plot for age and observed a positive correlation trend in Figure 8a. We further investigated the SHAP interaction between features shown in Figure 6, which segments the SHAP value into the main value on the diagonal line and the interaction value off the diagonal line. In Figure 7, in the heat map, which is the absolute mean calculated from all the instances, we observe that the highest point is the main age value of 0.238. We further checked the dependency graph in Figure 8b and observed a very clear positive correlation between age and SPSC. However, we observed no special trend with respect to interactions, as shown in Figure 8c,d. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Cost of FN** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1 Score** & **ROC Area** & **PRC Area** & **Acc.** & **Category** \\ \hline \multirow{2}{*}{1} & 0.853 & 0.048 & 0.947 & 0.853 & 0.897 & 0.939 & 0.932 & \multirow{2}{*}{0.902} & non-SPSC \\  & 0.952 & 0.147 & 0.866 & 0.952 & 0.907 & 0.939 & 0.926 & & SPSC \\ \hline \multirow{2}{*}{3} & 0.784 & 0.045 & 0.946 & 0.784 & 0.857 & 0.937 & 0.932 & \multirow{2}{*}{0.870} & non-SPSC \\  & 0.955 & 0.216 & 0.815 & 0.955 & 0.880 & 0.937 & 0.923 & & SPSC \\ \hline \multirow{2}{*}{5} & 0.728 & 0.041 & 0.947 & 0.728 & 0.823 & 0.938 & 0.932 & \multirow{2}{*}{0.843} & non-SPSC \\  & 0.959 & 0.272 & 0.779 & 0.959 & 0.860 & 0.938 & 0.924 & & SPSC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the random forest classification performance for different costs.