## 5 Results
### Influence of Network Size on Predictive Accuracy  
We evaluated the impact of network size on predictive accuracy by comparing models with varying proportions of genes, using different boruta_perc thresholds. Specifically, we assessed models at boruta_perc thresholds of 70% (Boruta-70), 80% (Boruta-80), and 90% (Boruta-90). The Boruta-80 model showed superior performance, as detailed in Table 6, achieving the highest AUC-ROC (\(0.8724\pm 0.0511\)), accuracy (\(0.7789\pm 0.068\)),  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **AUC-ROC** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline SVM & \(0.7252\pm 0.1513\) & \(0.6526\pm 0.1648\) & \(0.6443\pm 0.2133\) & \(0.5461\pm 0.3021\) & \(\textbf{0.936}\pm\textbf{0.0615}\) \\ DT & \(0.7529\pm 0.0918\) & \(0.6368\pm 0.0706\) & \(0.7143\pm 0.1425\) & \(0.7692\pm 0.3206\) & \(0.7638\pm 0.1097\) \\ RF & \(0.7788\pm 0.0448\) & \(0.6894\pm 0.0798\) & \(0.7552\pm 0.1359\) & \(0.7846\pm 0.2487\) & \(0.8000\pm 0.1121\) \\ GBDT & \(0.8064\pm 0.0276\) & \(0.7158\pm 0.1026\) & \(0.7575\pm 0.1313\) & \(0.7231\pm 0.2483\) & \(0.8591\pm 0.0613\) \\ GAT & \(0.7929\pm 0.0459\) & \(0.7105\pm 0.0588\) & \(0.7843\pm 0.0889\) & \(0.8231\pm 0.2046\) & \(0.7987\pm 0.1155\) \\ MSGAT & \(0.8102\pm 0.0907\) & \(0.7368\pm 0.0558\) & \(0.8195\pm 0.0446\) & \(0.8377\pm 0.0699\) & \(0.6988\pm 0.0328\) \\
**MVMSGAT** & \(\textbf{0.8724}\pm\textbf{0.0511}\) & \(\textbf{0.7789}\pm\textbf{0.0680}\) & \(\textbf{0.8529}\pm\textbf{0.0338}\) & \(\textbf{0.9231}\pm\textbf{0.0719}\) & \(0.8038\pm 0.0955\) \\ \hline \hline \end{tabular} AUC-ROC stands for Area Under the Receiver Operating Characteristics Curve. This metric is used to evaluate the performance of a binary classifier. Accuracy, F1 score, recall, and precision are additional metrics for assessing the effectiveness of the predictive models. The values are presented as mean \(\pm\) standard deviation. **Bold** values indicate the highest performance metrics in each category for easy comparison.  
\end{table}
Table 4: Comparison of Model Performance Metrics for Predicting the Efficacy of Neoadjuvant Chemotherapy in Bladder Cancer (Table 3).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **AUC-ROC** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline co-ex & \(0.7654\pm 0.0323\) & \(0.7210\pm 0.0546\) & \(0.7856\pm 0.0899\) & \(0.8231\pm 0.2007\) & \(0.7973\pm 0.1213\) \\ ppi & \(0.8519\pm 0.0321\) & \(0.7105\pm 0.0456\) & \(0.8238\pm 0.0225\) & \(\textbf{0.9846}\pm\textbf{0.0211}\) & \(0.7094\pm 0.0401\) \\
**co-ex + ppi** & \(\textbf{0.8724}\pm\textbf{0.0511}\) & \(\textbf{0.7789}\pm\textbf{0.0680}\) & \(\textbf{0.8529}\pm\textbf{0.0338}\) & \(0.9231\pm 0.0719\) & \(\textbf{0.8038}\pm\textbf{0.0955}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of graph-based models.  
F1 score (0.8529 \(\pm\) 0.0338), recall (0.9231 \(\pm\) 0.0719), and precision (0.8038 \(\pm\) 0.0955). Conversely, the Boruta-70 model had lower performance, with an AUC-ROC of 0.8346 \(\pm\) 0.0278, accuracy 0.6789 \(\pm\) 0.0706, F1 score 0.7619 \(\pm\) 0.0892, recall 0.8000 \(\pm\) 0.2266, and precision 0.7693 \(\pm\) 0.0778. The Boruta-90 model, though the most selective, displayed an AUC-ROC of 0.8256 \(\pm\) 0.0262, accuracy 0.7526 \(\pm\) 0.0235, F1 score 0.831 \(\pm\) 0.0266, recall 0.9000 \(\pm\) 0.1108, and precision 0.7827 \(\pm\) 0.0557. Overall, the Boruta-80 model, with a moderately sized gene network, provides the optimal balance between sensitivity and specificity, leading to the best predictive performance for evaluating the efficacy of neoadjuvant chemotherapy in bladder cancer.