## 3 Results
### Interpretation Results
#### 3.2.2 NLP  
In relation to NLP processing, we present two case studies with the following clinical characteristics. In the first case, the patient has no documented history of CAD, diabetes, or chronic kidney disease. There is no record of the patient having undergone Percutaneous Coronary Intervention (PCI), and the Electrocardiogram (ECG) results are normal. However, the patient exhibits symptoms related to angina. The patient is a male over 40. Both medical experts and the trained RGB-CNN correctly classified this instance as pathological. In the second case, the patient does not have any known history of chronic kidney disease or angina and has no record of having undergone an ECG procedure. Nevertheless, the patient displays symptoms associated with previous CAD, PCI, and diabetes. Additionally, the patient is a male over the age of 40. Both medical professionals and RGB-CNN accurately diagnosed the patient with CAD. The authors introduced the case studies into GPT-3.5 by creating a text prompt, as shown in Figure 6. This prompt includes a description of our proposed system and allows NLP to evaluate the DeepFCM's performance, the produced concept vector, and the clinical characteristics, regarding the first case study. In Figures 7 and 8, we present the texts generated by GPT-3.5 for the two case studies accordingly, where GPT-3.5 provided a detailed analysis of the relationship of each clinical characteristic with the output. We can observe that the produced texts offer nuclear experts an interpretable and thorough demonstration of patient status and provide the logic behind DeepFCM's predictive capabilities. GPT-3.5 facilitates the transformation of DeepFCM into a transparent and trustworthy tool suitable for integration into the decision-making process for CAD diagnosis. Figure 5: Representation of Grad–CAM application to normal polar maps: (**a**) first TN case study, (**b**) second TN case study, (**c**) first False Negative (FN) case study, (**d**) second FN case study. Figure 6: Representation of text prompt applied to GPT-3.5 regarding the first case study. Based on the input concept values you provided, it seems that the patient does not have a history of CAD or PCI. The patient does not have diabetes or chronic kidney disease. However, they do experience angina-like pain, which could be a symptom of CAD. The patient's ECG result is not mentioned, so we cannot determine its impact on the diagnosis. The patient is male, and their age is not specified to be below 40 years. The expert diagnosis and the polar map classification are both positive, indicating that the human expert and the CNN model predict the presence of CAD. This aligns with the output class of 1, which suggests that the patient is indeed suffering from CAD. It is important to note that the weight matrix values indicate the strength of the relationships between the input concepts and the output class, but they are not directly interpretable in verbal language. Instead, we focus on the presence or absence of certain input concepts and their overall impact on the diagnosis. In this case, the presence of angina-like pain, positive expert diagnosis, and positive polar map classification contribute to the diagnosis of CAD. Based on the given input concept values and the weight matrix, the FCM predicts that the patient is suffering from CAD. Let's analyze the contributing factors. The presence of Known_CAD, Previous_PCI, and Diabetes all have positive weights, indicating that these factors increase the likelihood of CAD. Known_CAD suggests that the patient has a history of CAD, while Previous_PCI suggests they have undergone a Percutaneous Coronary Intervention. The presence of Diabetes is also known to be a risk factor for CAD. On the other hand, Chronic_kidney_disease and Angina_like have zero weights, implying that they do not significantly influence the prediction.