## 4 Discussion  
In this research study, we developed an automatic FCM-based model to detect CAD in patients. The dataset included 346 normal and 248 pathological cases, which nuclear experts had initially characterized for classification purposes. Clinical and imaging data (polar maps) were included. Feature selection was applied to improve the results by retaining the features with a higher impact on the final diagnosis and discarding the features that introduced noise and redundancy into the model. For further enhancement of the FCM, we developed RGB-CNN, a lightweight model for CAD diagnosis exploiting CNN abilities in extracting high-dimensional features from the available images. This incorporation of CNN predictions along with historical data constructed the proposed model, DeepFCM, which demonstrated considerable promise in enhancing accuracy and interpretability to healthcare professionals. The proposed model achieved 83.07 \(\pm\) 4.72% accuracy, 0.17 loss, and 86.21%, 79.99%, and 81.78% for sensitivity, specificity, and precision, respectively. With the application Grad-CAM, RGB-CNN's computations became interpretable and transparent, enhancing the model's explainability for research applications. Furthermore, the results obtained from DeepFCM were integrated into the NLP model (GPT-3.5) to enhance the interpretability of the findings. Figure 8: Demonstration of GPT-3.5 results regarding the second case study. Figure 7: Demonstration of GPT-3.5 results regarding the first case study. DeepFCM can elucidate intricate cause-and-effect relationships among symptoms, diseases, and treatments inherently. The key to understanding these relationships lies in the interpretation of the weight matrix within the FCM. The weight matrix captures the strength and direction of influences between different nodes or concepts, offering doctors a transparent and interpretable framework to analyze how various factors contribute to a patient's condition. For example, DeepFCM learned that the diagnosis of the expert, which was an integrated feature of the model, should affect the output by a weight of 0.88, which is the largest observed weight in Table 3. This fact indicates that DeepFCM distinguishes the human expert as the most vital contributor to the result, thereby maintaining the doctor-in-the-loop approach. The weight matrix implied an inner relationship between the ECG outcome and the gender of the patient. More specifically, it was found that when the patient is male, the weight of the ECG should be slightly strengthened (Table 3). This connection is indeed documented in the literature [49], where it was found that men exhibit higher sensitivity in the ECG test. Finally, an interesting observation is that the CNN's prediction on the polar maps strongly affects the output (0.75). To summarize, DeepFCM suggests that the following four factors constitute essential predictors: the human expert's diagnostic yield, the characterization of the polar maps, as provided by the RGB-CNN of the framework, the patient's history regarding CAD, and the presence of angina-like symptoms. Grad-CAM provided insights into the decision-making process of RGB-CNN by highlighting the regions of the polar maps that are most influential in making a particular classification or diagnosis. It was observed that, in most of the cases, the produced heatmaps indicated decisive features in areas where the polar map was initially green, which is considered acceptable. In healthy subjects, Grad-CAM produced mainly blue heatmaps, which implies no decisive features toward the positive class. However, there were some cases where the produced heatmap pointed out irrelevant areas. Although such cases undermine the performance of DeepFCM, they can be exploited by the medical staff to guess a potential mistake in the framework. For example, a DeepFCM prediction that was based on an inconclusive heatmap can be considered non-reliable by human experts.