## 4 Experiment and Results
### Evaluation Metrics  
The experiments in this paper provide a comprehensive evaluation of the model's performance using various fundamental assessment metrics and curves more suitable for evaluating medical classification models. The specific metrics are as follows:  
1. Basic Evaluation Metrics: The basic evaluation metrics, including the AUC [37], accuracy (ACC) [38], specificity (SPE) [39], sensitivity (SEN) [38], positive predictive value (PPV) [39], negative predictive value (NPV) [39], and F1-score [38], were calculated based on the results from model validation. These metrics provide a comprehensive assessment of the model's overall predictive performance. 2. Matrix [38] and Precision-Recall (P-R) Curve [40]: The confusion matrix and P-R curve provide valuable insights into the model's performance, especially in imbalanced class distributions. The confusion matrix breaks down the classification results into true positives, true negatives, false positives, and false negatives, allowing for the calculation of metrics like precision, recall, and F1-score, which highlight the model's ability to correctly identify the positive class. The P-R curve evaluates the trade-off \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Library Name** & **Version Number** & **Library Name** & **Version Number** \\ \hline python & 3.9.13 & pylint & 2.14.5 \\ numpy & 1.24.4 & scikit-learn & 1.0.2 \\ pandas & 2.2.2 & torch & 2.2.1+cu118 \\ pylidc & 0.2.3 & tqdm & 4.64.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental software environment. between precision and recall at different thresholds, providing a clearer view of the model's performance in detecting minority (positive) cases. Together, these tools complement the limitations of basic metrics and ensure good performance of the model on the positive class. 3. Calibration Curve [41]: The calibration curve is used to assess the alignment between the predicted probabilities and the actual occurrence probabilities of a classification model. In the calibration curve, the x-axis represents the predicted risk, while the y-axis represents the observed risk. By comparing the position of the calibration curve with the 45-degree diagonal line, the calibration performance of the model can be evaluated. This reflects the model's accuracy in predicting the probability of an event occurring. To quantify uncertainty, we calculated 95% confidence intervals (CIs) for the calibration curves using the bootstrap method, which were then visualized as shaded areas around the calibration curves. This approach provides a comprehensive assessment of the model's calibration performance and the uncertainty associated with predicted probabilities. 4. DCA Curve (Decision Curve Analysis) [42]: Decision Curve Analysis (DCA) is a method for evaluating the clinical utility of classification model predictions under various disease risk thresholds. The x-axis represents the threshold probability, and the y-axis represents the net benefit. DCA evaluates the utility of different models and disease risk thresholds, assisting in the selection of the most suitable classification model and the optimal threshold. The clinical utility of the model is typically assessed by calculating the area under the curve (net benefit) and comparing the DCA curves across different models. Similarly to the calibration curve, a similar approach was adopted in plotting the DCA curve to further quantify uncertainty, with the uncertainty also visualized as shaded areas around the DCA curve. 5. Brier Score [43] and Expected Calibration Error (ECE) [44]: To further quantify the uncertainty in model predictions, we introduce two key uncertainty quantification metrics: the Brier score and ECE.