## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.4 Ablation Experiment  
An ablation experiment was conducted using Group B as an example to validate the effectiveness of multi-dimensional features and ensemble learning strategies. To minimize potential errors, the model that did not use the ensemble learning strategy was evaluated using Random Forest (RF). As shown in Table 8, the first row presents the validation results when the model is trained using only the clinical annotated features. The second row shows the fusion of selected radiomics features with clinical annotated features. This feature fusion, which integrates information from different scales, resulted in a 1.64% improvement in the ACC and a 1.5% improvement in the SPE. The third row represents the fusion of deep features with clinical annotated features, but the performance was sub-optimal, with all three metrics exhibiting a decline. The fourth row shows the fusion of all three feature types (clinical annotated, radiomics, and deep features), resulting in a hybrid feature set. Although the validation metrics in this configuration were slightly inferior to those in the second row, the model performed well by considering features from all dimensions. Finally, the fifth row demonstrates the model trained using ensemble learning with multi-dimensional features. All evaluation metrics reached their optimal values in this case, indicating that the proposed algorithm significantly improved performance compared to conventional methods. The ACC improved by 1.65% or more, and the SPE improved by 1.49% or more. Next, the evaluation metrics predicted by each individual classifier on the test set are presented to further validate the effectiveness of the model ensemble strategy, as shown in Tables 9 and 10. The results demonstrate that, while the model ensemble does not achieve the optimal performance on each individual metric, the average performance across multiple metrics is significantly improved compared to the performance of the individual classifiers. Specifically, the evaluation metrics for each ensemble model are consistently higher than the average of the three individual classifiers, confirming the stability and effectiveness of the ensemble approach. Finally, the calibration curves and DCA curves for the four groups of multi-dimensional feature models are shown in Figures 9 and 10, respectively. As seen in the calibration curves, all four group curves gradually approach the black dashed line, representing perfect calibration, and then converge around it with some fluctuations. The results of the voting ensemble model (red with stars) are closer to the reference line, particularly in the predicted probability range of 0.4-0.8. Moreover, the curve of the ensemble model is relatively smoother, unlike some individual classifiers (such as GBM), which exhibit more noticeable fluctuations. This suggests that the ensemble model provides more stable calibration across different prediction probability levels, reflecting the actual situation more reliably and reducing calibration errors caused by fluctuations in predicted probabilities. Additionally, the 95% confidence interval for the voting ensemble model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Clinical** & **Radiomics** & **Deep** & **Ensemble** & **AUC** & **ACC** & **SPE** \\
**Features** & **Features** & **Features** & **Learning** & & & \\ \hline \(\surd\) & & & & 0.9908 & 0.9456 & 0.9302 \\ \(\surd\) & \(\surd\) & & & 0.9941 & 0.9620 & 0.9452 \\ \(\surd\) & & \(\surd\) & & 0.9807 & 0.9354 & 0.8804 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.9910 & 0.9528 & 0.9169 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.9976 & 0.9785 & 0.9601 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experiment of the algorithm in this paper on the dataset (Group B). “\(\surd\)” indicates that the relevant strategy is adopted. (red shaded area) is narrower compared to most individual classifiers and does not show extreme wide fluctuations.