## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.3 Model Evaluation
A comparison of various performance metrics is shown in Table 7. As shown in the table, the proposed algorithm, with Group B and Group D metrics (ACC = 0.98, SPE = 0.96, SEN = 0.98), outperforms all the compared algorithms. This demonstrates the superiority of the multi-dimensional features fusion approach, which combines deep features, radiomics features, and One-Hot-encoded clinical annotated features. Although some comparative studies outperform this paper on a single metric, their ACC and SPE metrics are significantly inferior to the results of the model presented in this paper. In comparison to other mainstream algorithms from the past three years, the proposed method achieves a well-balanced performance across all evaluation metrics, with notably higher accuracy, thereby proving its competitive edge in overall performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & **AUC** & **ACC** &
\begin{tabular}{c} **Brier Score** \\ **(95\% CIs)** \\ \end{tabular} & **ECE** & **SEN** & **SPE** & **PPV** & **NPV** \\ \hline A & 0.9885 & 0.9497 & (0.0330, 0.0446) & 0.0781 & 0.8854 & 0.9725 & 0.95 & 0.9452 \\ B & 0.9976 & 0.9785 & (0.0151, 0.0232) & 0.0922 & 0.9601 & 0.9866 & 0.9823 & 0.9698 \\ C & 0.9919 & 0.9579 & (0.0267, 0.0369) & 0.0562 & 0.9352 & 0.9681 & 0.971 & 0.929 \\ D & 0.9974 & 0.9779 & (0.0142, 0.0217) & 0.0726 & 0.9651 & 0.9829 & 0.9844 & 0.9619 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction metrics of ensemble models. Figure 5: Confusion matrix. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Structure**} & \multirow{2}{*}{**Source/Year**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\  & & **(LIDC-IDRI)** & \\ \cline{2-5}  & **ACC** & **SPE** & **SEN** \\ \hline Structured Features & & & & \\ + CNN + XGBoost [9] & Information Fusion/2022 & 0.94 & 0.94 & 0.93 \\ \hline CNN + Adaptive Morphology & Biomedical Signal Processing & & & \\ + Dual-path [46] & and Control/2022 & 0.97 & 0.98 & 0.93 \\ \hline
3D Multiscale Cross & Computer Engineering and & & & \\ Fusion Network [47] & Applications/2022 & 0.90 & 0.88 & 0.93 \\ \hline Multigranularity Transformer & & & & \\ + LFS [48] & Applied Sciences/2022 & 0.96 & 0.96 & 0.98 \\ \hline Multimodal Feature & Computer Engineering and & & & \\ + Fusion Network [49] & Applications/2023 & 0.93 & 0.95 & 0.91 \\ \hline Dilated Convolution & Biomedical Signal Processing & & & \\ + Multiscale [50] & and Control/2023 & 0.95 & 0.96 & 0.95 \\ \hline Self-supervised Learning & Expert Systems with & & & \\ + Transfer Learning & Applications/2023 & 0.92 & 0.93 & 0.91 \\ + Visual Attention [51] & Cancers/2023 & 0.92 & / & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with mainstream algorithms. Figure 6: Precision–recall curve. (3) Interpretability Validation  
Some outstanding studies [60] have utilized visualization and comparison of the weights of features at different scales, using this strategy as an interpretability tool to clearly identify and validate the contribution of relevant features to the model's predictions. Next, we will explore and validate the interpretability of deep features by plotting bar charts showing the weights of three types of features in the prediction process. The average feature weights from all individual classifiers were calculated to represent the feature weight for the ensemble model. To preserve the weight differences in the same feature across different classifiers, the standard deviation was computed to reflect any inconsistencies in how the classifiers evaluated that feature. This approach provided a more comprehensive view of feature importance within the ensemble model. The feature weight plots for individual classifiers can be found in the Appendix A. The plots for the four ensemble models are shown in Figure 7, with the black line segments representing the standard deviation. After feature selection and fusion, a total of 39 features were retained (7 clinical annotated features, 12 radiomics features, and 20 deep features).