## 3 Results  
### Classification Results  
The DeepFCM methodology achieved an accuracy of 83.07%, with a sensitivity of 86.21% and a specificity of 79.99% (refer to Table 2). For the sake of comparison, we also provide the results of the standalone RBG-CNN model, which solely processes the images. Additionally, we showcase the performance of DeepFCM when it utilizes the full feature set, rather than just the selected features. We further present the diagnostic yield of doctors, which is based on their visual inspection of the polar maps and consideration of the clinical data. Readers need to note that the benchmarks for all these comparisons are the results from invasive coronary angiography.  
It is demonstrated that DeepFCM applied to the optimal subset performed remarkably and achieved higher performance metrics. The integration of both imaging and clinical data surpassed the RGB-CNN model and expert diagnosis. Furthermore, feature selection enhanced the results by leading to a more effective representation of the dataset, as we can see by comparing the 83.07% accuracy with the optimal subset and 76.1% with the total dataset. The results showcase the promising potential of the hybrid DeepFCM model. The proposed model, DeepFCM, applied to the optimal subset attained average accuracy, with a mean of 83.07% and a small standard deviation of \(\pm\)4.72%. The low loss value of 0.17 reflects the model's robust convergence during training. The sensitivity of 86.21% and specificity of 79.99% signify its competence in identifying CAD-positive and CAD-negative case studies, respectively. Moreover, a precision of 81.78% highlights the model's capability to accurately classify true positive cases.  
In Table 3, we demonstrate the interconnection among concepts in the first column, the range of values provided by experts for the initialization of interconnection to be randomly selected in the second column, and the weight value that was produced from DeepFCM for the according interconnection in the third column. We can observe that the DeepFCM applied to the optimal subset generated efficient results that are close to the initial range with small deviations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Run** & **Accuracy** & **Loss** & **Sensitivity** & **Specificity** & **Precision** \\ \hline Expert diagnosis & 78.91 & 0.21 & 77.9 & 79.7 & 75.09 \\ RGB–CNN & 75.42 \(\pm\) 4.54 & 0.47 & 80.53 & 66.38 & 66.39 \\ DeepFCM optimal subset & 83.07 \(\pm\) 4.72 & 0.17 & 86.21 & 79.99 & 81.78 \\ DeepFCM all features & 76.1 \(\pm\) 5.52 & 0.24 & 77.79 & 72.78 & 71.67 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of results among expert diagnosis, RGB–CNN applied to images only, and DeepFCM applied to the optimal subset and total dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Relationship of Concepts** & **Transformed Linguistic** & **Produced Values from** \\  & **Values to Ranges** & **DeepFCM** \\ \hline Known CAD\(\rightarrow\)Output & [0.5, 0.85] & 0.68 \(\pm\) 0.08 \\ Previous PCI\(\rightarrow\)Output & [0.15, 0.5] & 0.34 \(\pm\) 0.09 \\ Diabetes\(\rightarrow\)Output & [0.5, 0.85] & 0.63 \(\pm\) 0.1 \\ Chronic Kidney & [0.15, 0.5] & 0.32 \(\pm\) 0.009 \\ Disease\(\rightarrow\)Output & [0.5, 0.85] & 0.67 \(\pm\) 0.12 \\ ECG\(\rightarrow\)Output & [0.35, 0.65] & 0.43 \(\pm\) 0.08 \\ Male\(\rightarrow\)ECG & [0.15, 0.5] & 0.17 \(\pm\) 0.1 \\ Expert Diagnosis\(\rightarrow\)Output & [0.7, 1] & 0.88 \(\pm\) 0.1 \\ CNN\(\rightarrow\)Output & [0.5, 0.85] & 0.75 \(\pm\) 0.09 \\ \hline \end{tabular}
\end{table}
Table 3: Presentation of extracted ranges for the relationship between concepts produced from the DeepFCM model applied to the optimal subset and comparison with expert knowledge.