###### Abstract  
Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model's predictions, enhancing transparency. By the SHapley Additive exPlanations' interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication. Keywords:renal cell carcinoma; machine learning; survival prediction; multimodal data +
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& 61.72 \(\pm\) 0.25 & 62.40 \(\pm\) 0.49 & 61.72 \(\pm\) 0.25 & 61.58 \(\pm\) 0.24 \\  & Test & 61.78 & 62.41 & 61.78 & 61.68 \\  & Training & 61.82 \(\pm\) 0.16 & 62.33 \(\pm\) 0.28 & 61.82 \(\pm\) 0.16 & 61.74 \(\pm\) 0.18 \\  & Test & 61.63 & 62.11 & 61.63 & 61.57 \\  & Training & 61.71 \(\pm\) 0.27 & 61.78 \(\pm\) 0.31 & 61.71 \(\pm\) 0.27 & 61.73 \(\pm\) 0.28 \\  & Test & 62.33 & 62.42 & 62.33 & 62.35 \\  & Training & 61.71 \(\pm\) 0.21 & 61.91 \(\pm\) 0.25 & 61.71 \(\pm\) 0.21 & 61.71 \(\pm\) 0.24 \\  & Test &
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Figure 5: Feature importance of RF in stage 1. Figure 6: Feature importance of GB in stage 1. Local interpretability enables the analysis of input characteristics for individual samples across various models, facilitating specific predictions for each case. The left panels of Figures 9 and 10 depict the survival prediction for a particular sample. As illustrated in the left panel of Figure 9, the variables M stage, Tumor Size, and Surgery Cancer all have a positive impact on this sample's survival probability, whereas the variable Age_old has a negative effect. In a similar vein, the left panel of Figure 10 shows that Tumor Size, M stage, and Grade_2 positively influence the survival prediction. Consequently, the model predicts survival for this sample patient. Conversely, the right panels of Figures 9 and 10 present the survival prediction plot for a different sample by both models. It is apparent from these figures that Tumor Size and Age_old are significant factors, with their prominence leading the model to predict a non-survival outcome for this patient. Figure 8: Top 6 features’ dependence plot of RF. Figure 7: Top 6 features’ dependence plot of GB. To quantify the exact impact of the important variables derived from the SHAP plotting on the classification results, we use Uplift Models to validate the important variables with high rank in a new round.