## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.3 Model Evaluation  
In this experiment, clinical annotated feature encoding was performed using two methods: binarization and One-Hot encoding. The pre-trained networks used for transfer learning were ResNet-50 and AlexNet. For clarity and consistency, the experimental groups are defined in Table 5:  
1. Prediction Metrics The ACC, SPE, SEN, PPV, and NPV of the ensemble model were calculated on the test set to evaluate its predictive performance. The corresponding metrics are summarized in Table 6. The experimental results demonstrate that the ensemble model performed excellently across multiple evaluation metrics. Specifically, for Group B and Group D, the following results were achieved: AUC: 99.76% and 99.74%, respectively; ACC: 97.85% and 97.79%, respectively; SEN: 96.01% and 96.51%, respectively; SPE: 98.66% and 98.29%, respectively; PPV: 98.23% and 98.44%, respectively; NPV: 96.98% and 96.19%, respectively. These results preliminarily validate the effectiveness of the proposed method in enhancing both the stability and accuracy of the model's predictions. In addition, the model's uncertainty quantification metrics (Brier score and ECE) further validate its predictive reliability: the Brier scores for the four model groups remain stable around 0.03, which is an excellent score, indicating a small mean squared error between the predicted probabilities and actual outcomes. The ECE values range from 0.0562 to 0.0922, suggesting that the average deviation between the model's predicted probabilities and the actual positive rate is within an acceptable range, demonstrating good calibration performance. These two metrics together show that the model maintains high predictive accuracy while offering reasonable uncertainty estimation, providing a robust probabilistic basis for decision-making in practical applications. To provide a more comprehensive view of the model's performance across each class, we present the confusion matrix, as shown in Figure 5. In this study, the dataset contains significantly more negative class samples than positive class samples, which results in relatively higher predicted values for the negative class in the confusion matrix (with darker colors in the TN region). Despite the model's overall excellent performance and a high proportion of correct classifications, the higher number of negative class samples means the model has stronger identification capability for negative samples, which may lead to relatively weaker predictions for the positive class. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Clinical Annotated**} & \multirow{2}{*}{**Radiomics**} & \multirow{2}{*}{**Deep Features**} \\ \cline{2-2} \cline{4-5}  & & **Features** & & & **Features** \\ \cline{2-2} \cline{4-5}  & **Binarization** & **One-Hot** & & & **ResNet50** & **AlexNet** \\ \hline A & \(\surd\) & & \(\surd\) & \(\surd\) & \\ B & & \(\surd\) & \(\surd\) & \(\surd\) & \\ C & \(\surd\) & & \(\surd\) & & \(\surd\) \\ D & & \(\surd\) & \(\surd\) & & \(\surd\) \\ \hline \end{tabular}
\end{table} 
Table 5: Settings of different feature—fusion approaches. ”\(\surd\)” represents the fusion features of the corresponding group. Due to the smaller number of positive class samples in the dataset, simple metrics like the AUC and ACC cannot fully capture the model's performance on the positive class. Therefore, we further evaluate the model's performance on the minority class (positive class) using the P-R curve, as shown in Figure 6. From the figure, it is evident that the four model groups (red curves with stars) perform exceptionally well in terms of the AP metric. In areas with low recall, the models maintain high precision, indicating that the model continues to perform well in identifying minority class samples. 2. Comparative Validation  
The algorithm proposed in this paper was compared with state-of-the-art algorithms from relevant research conducted in the past three years, both domestically and internationally.