### Stage II: Regression and Interpretable Analysis  
After the analysis in the previous stage, we select the data of patients who died of RCC in the multimodal dataset for the prediction of survival time with different types of regression models. Similar to stage 1, we use the same machine learning algorithm from stage 1 one by one. For Stacking and Blending, we use linear regression as the meta model instead of logistic regression. The metrics of the predicted survival time results are shown in Table 8. Although the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) show relatively large errors, we are only interested in the reclassification results from the regression results. We categorize the predicted survival times of all models into 1, 2, 3, 4 and 5 years. These reclassification results are shown in Table 9 and visualized in Figure 11 for observation and analysis. The ADA model performs the worst in terms of RCC death prognosis at 1-5 years. In contrast, the ET model outperforms all models in terms of prognosis at 2-5 years for RCC. Although RF performs well in step 1, it fails to have higher accuracy than other models at 1-3 years' prognosis. The Blending model maintains relatively stable accuracy over the 1-5 year prognosis and performs relatively well over the 1-5 year prognosis. However, it has very poor prognosis results at 5 years, which are only higher than ADA. Overall, classical machine learning models are more unstable in the 1-5 year prognosis, while for fusion models, the overall performance is smoother. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Correlation** & **Uplift Score** &
\begin{tabular}{c} **Qini** \\ **Coefficient** \\ \end{tabular} & **Accuracy** \\ \hline M stage & \(-0.40\) & \(0.81\) & \(-11.79\) & \(73.99\%\) \\ Age\_old & \(-0.24\) & \(0.39\) & \(6.11\) & \(76.92\%\) \\ Histologic type\_8317/3 & \(0.27\) & \(0.47\) & \(0.95\) & \(76.95\%\) \\ Age\_middle & \(0.23\) & \(0.35\) & \(-0.22\) & \(76.38\%\) \\ Surgery\_cancer & \(0.27\) & \(0.53\) & \(4.57\) & \(74.57\%\) \\ Grade\_2 & \(0.21\) & \(0.13\) & \(6.06\) & \(76.89\%\) \\ Histologic type\_8312/3 & \(-0.18\) & \(0.29\) & \(5.53\) & \(76.27\%\) \\ T stage\_1 & \(0.19\) & \(0.07\) & \(-1.41\) & \(76.89\%\) \\ Marital status\_Widowed & \(-0.14\) & \(0.25\) & \(3.93\) & \(75.23\%\) \\ Chemotherapy & \(-0.23\) & \(0.26\) & \(-8.26\) & \(72.20\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Feature importance ranking of RF Uplift Model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Correlation** & **Uplift Score** &
\begin{tabular}{c} **Qini** \\ **Coefficient** \\ \end{tabular} & **Accuracy** \\ \hline M stage & \(-0.40\) & \(1.00\) & \(-7.29\) & \(75.05\%\) \\ Age\_old & \(-0.24\) & \(0.42\) & \(12.74\) & \(77.31\%\) \\ Histologic type\_8317/3 & \(0.27\) & \(0.70\) & \(16.97\) & \(77.40\%\) \\ Surgery\_cancer & \(0.27\) & \(0.78\) & \(8.63\) & \(76.37\%\) \\ Age\_middle & \(0.23\) & \(0.34\) & \(18.04\) & \(77.12\%\) \\ T stage\_1 & \(0.19\) & \(0.39\) & \(18.04\) & \(77.13\%\) \\ Grade\_2 & \(0.21\) & \(0.28\) & \(18.22\) & \(77.82\%\) \\ Marital status\_Widowed & \(-0.14\) & \(0.36\) & \(12.11\) & \(76.21\%\) \\ Histologic type\_8312/3 & \(-0.18\) & \(0.37\) & \(16.76\) & \(76.98\%\) \\ Radiation & \(-0.23\) & \(0.68\) & \(-10.92\) & \(73.53\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Feature importance ranking of GB Uplift Model. Similar to stage 1, we also visualize the importance of the variables in the linear regression meta model of Stacking and Blending models. As shown in Figures 12 and 13, among the five base models, GB, XGB and ET have important roles in the Stacking and Blending regression. We visualize these three regression models' importance using SHAP, and their feature importance plots are shown in Figures 14-16.