## 4 Discussion  
The main focus of this work is to illustrate the decision process of a black-box computer-aided decision-making model. Within this scope, the proposed explainability analysis offers valuable insight as to which features were the most essential for the classification and how each datum contributed to the final outcome. Below, we will explain what these figures showcase and then we will discuss these findings from a medical viewpoint. Cohen effect sizes (Figure 4) and their relative SHAP values (Figure 5) showcase an increased degree of similarity when it comes to identifying the most influencing features of the dataset. In both cases, it seems the most impactful kind of information is whether the entry previously had a CAD condition. Additionally, two more features that appear to play a major role are whether the patient is diabetic and their sex. Specifically, if the patient is diabetic or is male, they are more likely to have CAD than otherwise. Unsurprisingly, all these features are well-documented risk factors for CAD [46; 47; 48; 49; 50; 51; 52; 53]. Other factors that also contribute much to the classification are the level of exercise of the entry (RST ECG) and age. Moreover, these plots highlight that patients who exercise little to never or are between 40 and 50 years of age seem to be more prone to CAD. Alternatively, individuals who engage in regular physical activity and are younger than 40 years old tend to have a lower susceptibility to CAD, as indicated by higher values of RST ECG. These findings are in line with the existing medical literature on cardiovascular diseases and their associated risk factors [46; 47; 48; 49; 50; 51; 52; 53]. The [PERSON] effect size chart leads to observations that not only match with the ones from the SHAP summary plot, but also perfectly align with the established knowledge in this field. The waterfall plots showcase the decision process for a NO-CAD subject (Figure 2) and a CAD one (Figure 3). These plots showcase how each feature \"pushes\" the decision towards the CAD or the NO-CAD class. This classification is based on the decision threshold, which for this problem is equal to -0.382. Each feature adds (equivalent of \"pushing\" right) or subtracts (equivalent of \"pushing\" left) to the prediction score, and eventually, if this is higher than the threshold of -0.382, the entry is classified as a CAD instance. In lieu of this information, a human doctor can assimilate the prediction process of the black-box model and determine the outcome by knowing which factors contributed to the prediction and by how much. By comparing the evaluation metrics (Table 3) of the two scenarios (entire dataset as input vs. dedicated feature subset as input), it is evident that the selected feature subset leads to a far better prediction accuracy. Not only does the information depicted in Table 3 indicate a more accurate model when using a subset, but the true positive rate (sensitivity) and true negative rate (specificity) increase too. Hence, when using a stripped-down dataset as input, the model isolates the true condition of the subjects far more accurately. It is especially important to have a better true positive rate, because false positive instances can lead to increased costs and congestion of the healthcare system. This increase in performance is logical, given the fact that this dedicated subset contains the more important features, while also missing out factors that may confuse the model or contribute less to the decision process. Moreover, the majority of the most common risk factors for CAD are present in the dedicated feature subset, which further contributes to the legitimacy of the feature selection process from the viewpoint of a human medical expert/doctor. Considering the existing body of research, it is evident that it primarily consists of studies that utilize image data from SPECT and PET scans in the context of CAD classification. Furthermore, some studies incorporate the medical expert's diagnosis as an input.