## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.4 Ablation Experiment
Although Random Forest performs better in this regard, it confirms the reliability and accuracy of the ensemble model's predictions, offering a certain advantage in quantifying uncertainty and providing more reliable probability forecasts and uncertainty assessments for real-world applications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**AlexNet**} & \multicolumn{2}{c}{**AUC**} & \multicolumn{2}{c}{**ACC**} & \multicolumn{2}{c}{**F1-score**} & \multicolumn{2}{c}{**Brier Score**} & \multicolumn{2}{c}{**ECE**} & \multicolumn{2}{c}{**SPE**} & \multicolumn{1}{c}{**NPVV**} \\ \hline \multirow{4}{*}{Group} & \multirow{4}{*}{C} & XGBoost & 0.9915 & **0.9584** & **0.9543** & **0.0310** & 0.0588 & 0.9673 & 0.9278 \\  & & GBM & 0.9900 & 0.9564 & 0.9486 & 0.0355 & 0.1185 & **0.9688** & **0.9301** \\  & & RF & 0.9859 & 0.9441 & 0.9232 & 0.0465 & 0.0965 & 0.9651 & 0.9199 \\  & & ME & **0.9919** & 0.9579 & 0.9512 & 0.0316 & **0.0562** & 0.9681 & 0.9290 \\  & & XGBoost & 0.9976 & 0.9769 & **0.9742** & **0.0172** & 0.0738 & 0.9829 & 0.9619 \\  & & GBM & **0.9977** & 0.9774 & 0.9709 & 0.0179 & 0.1678 & **0.9852** & **0.9666** \\  & & RF & 0.9939 & 0.9620 & 0.9573 & 0.0336 & 0.1041 & 0.9703 & 0.9342 \\  & & ME & 0.9974 & **0.9779** & 0.9703 & 0.0178 & **0.0726** & 0.9829 & 0.9619 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Verification results of Group C and Group D. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**ResNet50**} & \multicolumn{2}{c}{**AUC**} & \multicolumn{2}{c}{**ACC**} & \multicolumn{2}{c}{**F1-score**} & \multicolumn{2}{c}{**Brier Score**} & \multicolumn{1}{c}{**ECE**} & \multicolumn{1}{c}{**SPE**} & \multicolumn{1}{c}{**NPVV**} \\ \hline \multirow{4}{*}{Group} & XGBoost & 0.9883 & 0.9456 & 0.9162 & 0.0407 & 0.0889 & 0.9725 & 0.9351 \\  & GBM & 0.9869 & 0.9456 & **0.9206** & 0.0408 & 0.1027 & **0.9770** & 0.9351 \\  & RF & 0.9801 & 0.9374 & 0.9134 & 0.0565 & 0.1111 & 0.9614 & 0.9110 \\  & & ME & **0.9885** & **0.9497** & 0.9162 & **0.0385** & **0.0781** & 0.9725 & **0.9452** \\  & & XGBoost & 0.9963 & 0.9769 & **0.9709** & 0.0186 & **0.0472** & 0.9837 & 0.9634 \\  & & GBM & 0.9966 & 0.9785 & 0.9702 & **0.0159** & 0.1523 & 0.9866 & 0.9698 \\  & & RF & 0.991 & 0.9528 & 0.9412 & 0.0410 & 0.1329 & 0.9688 & 0.9293 \\  & & ME & **0.9976** & **0.9785** & 0.9702 & 0.0189 & 0.0922 & **0.9866** & **0.9698** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Verification results of Group A and Group B.  
Figure 9: Calibration curve. In the DCA curves, by comparing the net benefit areas under the threshold range for each curve, the blue and green curves exhibit smaller areas, while the purple curve shows insufficient stability. In contrast, the voting ensemble model (red with stars) performs the best, maintaining higher net benefits at higher threshold probabilities and offering better decision support, indicating higher clinical practicality or decision-making value. Moreover, the net benefit uncertainty of the voting ensemble model at the corresponding thresholds is significantly smaller, showcasing superior performance in quantifying uncertainty and providing more stable and reliable support for real-world decision-making.