## 4 Discussion
On the other hand, a meaningful heatmap indicates that DeepFCM has discovered important features and may be considered more reliable. The integration of the NLP model improved the informativeness of the system. It provided a verbal interpretation of the results. It went beyond raw numerical data and transformed them into meaningful and human-readable descriptions. By providing a verbal interpretation of the results, the NLP model bridges the gap between data and understanding. It offers clinicians or users the ability to grasp the significance of the numbers, translating statistical findings into comprehensible narratives or descriptions. However, the reader can observe its inherent limitations in performing an in-depth analysis of the provided data. For example, the model failed to analyze and discuss the effect of the patient's gender on the ECG concept. In Table 4, we have gathered all the DL medical-related state-of-the-art studies for comparison reasons. We can observe that the rest of the studies have reached efficient results with only image data as input, where CAD is demonstrated in SPECT-MPI format or in polar map images, where CNN training was applied. However, our proposed DeepFCM framework, although it did not exceed the performance of the previous literature studies in terms of accuracy, offers explainability and transparency of results, with the application of the FCM and the integration of clinical and imaging data, along with expert knowledge. By incorporating both imaging and clinical data, we demonstrate a holistic view of a patient's status, taking into account not only the image but also the medical history. The proposed model, DeepFCM, leverages expert knowledge as well, which enhances reliability. The research presents some noteworthy limitations that warrant discussion. First and foremost, this study's dataset, while sufficiently large to facilitate the experiments conducted, is derived exclusively from a single hospital. This mono-centric data source inherently introduces limitations in terms of its representativeness and generalizability. The healthcare landscape can exhibit considerable regional variations in patient demographics, treatment protocols, and disease prevalence. Consequently, relying solely on data from one hospital can potentially skew the findings and limit their applicability to a broader population. Another critical aspect that warrants further investigation pertains to the efficiency and effectiveness of the CNN model, a pivotal component of the DeepFCM framework. While the integration of CNNs in FCMs holds promise for a wide range of applications, including medical image analysis and decision support systems, there remains a need for in-depth research to assess and optimize the performance of this fusion. The DeepFCM framework presents immediate potential for revolutionizing the routine diagnosis of CAD in clinical practice. Its unique combination of explainability and commendable classification accuracy makes it a compelling tool for healthcare professionals. DeepFCM not only offers robust classification accuracy but also provides transparent and understandable insights into its decision-making process, which is vital for building trust and facilitating the adoption of AI-driven solutions in healthcare. With the ability to pinpoint the critical factors contributing to CAD diagnosis, doctors can make more informed decisions and tailor treatment plans more effectively. This immediate potential signifies a