## 5 Conclusions  
The main objective of this work is to provide an explainable model that sheds light on a black-box prediction system for predicting CAD classification by offering an explainability aspect for its decision process. By utilizing SHAP analysis and Cohen effect sizes for the features, we highlight exactly which features contributed the most to the prediction mechanism. These findings can be of great significance because they make predictions about something more than just an outcome. The human user would be able to access the reasoning behind the prediction of the AI model and assess it accordingly. Ergo, it makes it more feasible and easier to facilitate a much-needed trust factor between the AI model and its human medical user. This trust is paramount, especially in the medical field, given the impact of such a prediction.  
Another key takeaway of this study is that the AI prediction system shows an almost complete alignment with the contemporary literature and research on the matter of CAD. The most common risk factors for CAD [37; 38; 39; 40; 41; 42; 43; 44] show substantial SHAP values and Cohen effect sizes. Certainly, this significantly boosts confidence, particularly when the model generates unexpected outputs. Additionally, the explainability outcomes offer an aspect of  
Figure 5: SHAP summary plot depicting the relative importance/SHAP values of input features.  
legitimacy to the specific computer-aided decision-making system since they are aligned with the findings of years of research on the matter. However, it is worth noting that some well-known risk factors for CAD, such as dyslipidemia, were consistently overlooked during the feature selection process.  
Future plans include incorporating data from images or the prediction results of AI models utilizing such data into the AI prediction model. Image results from SPECT and PET tests hold important information that can almost certainly enhance the prediction accuracy of such a system. Furthermore, it could be interesting to further research why some well-known CAD risk factors were absent from the feature selection and creation of the dedicated feature subsets (e.g., arterial hypertension). Additionally, there is an ongoing and robust discussion in the field about the methods and techniques to make AI models even more interpretable and trustworthy. This is indeed an area that requires further research, and we plan to explore additional methods to enhance model explainability in our future work.  
Design and conduction of the study: A.-D.S. and S.M.; data collection: I.D.A., N.P. and E.P.; data processing: A.-D.S. and I.D.A.; manuscript preparation: A.-D.S., S.M. and I.D.A.; manuscript review: N.P. and E.P.; medical expertise: N.P.; supervision, N.P., S.M. and E.P.; project administration, E.P. All authors have read and agreed to the published version of the manuscript.  
This research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \"2 nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers\" (Project Number: 3656).  
The ethical committee of the University General Hospital of Patras has greenlighted this particular data recording (Ethical & Research Committee of University Hospital of Patras--protocol number 108/10-3-2022).  
This study is retrospective in its nature and hence the requirement of obtaining informed consent from the participants was waived. The anonymity of the subjects was maintained during the data acquisition process. Furthermore, all procedures in this study are in accordance with the Declaration of Helsinki.  
Upon reasonable request, the data used in the current study can be available for sharing.  
The authors declare no conflict of interest.