## 2 Materials and Methods  
### Patient Population  
This study encompasses a comprehensive dataset comprising 571 participants. Within this population, 248 patients are confirmed to have CAD based on ICA testing, accounting \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Author (First)** & **Nature of Data** & **Results** & **Remarks** \\ \hline \multirow{4}{*}{[PERSON] [25]} & demographic, patient & Best of all—Random Forest: & \\  & history, doctor’s & Accuracy: 83.02\% & \\  & diagnosis, medical & Sensitivity: 83.06\% & \\  & & Specificity: 85.49\% & \\  & & Accuracy: 85.47\% & \\ [PERSON] [20] & demographic, patient & Sensitivity: 89.3\% & Input contains image data \\  & history, medical & Specificity: 79.31\% & from scans \\  & & AUC: 82.45\% & \\  & & Accuracy: 75.79\% & \\ [PERSON] [26] & demographic, patient & Sensitivity: 74.07\% & Input contains image data \\  & & history, medical & Specificity: 76.16\% & from scans \\  & & AUC: 71.09\% & \\ [PERSON] [27] & demographic, patient & Accuracy: 78.21\% & \\  & & history, medical & Sensitivity: 83.95\% & \\  & & Specificity: 68.96\% & \\  & & Best of all—Random forest & \\  & & & Accuracy: 92.04\% & Input contains image data \\  & & Sensitivity: 86.5\% & from scans \\  & & Specificity: 83.34\% & \\  & & Best of all—SVM: & \\ [PERSON] [29] & demographic, medical, & Accuracy: 95.45\% & Input contains image data \\  & lab results & Sensitivity: 95.91\% & from scans \\  & & Specificity: 91.66\% & \\  & & CAD & \\  & & Sensitivity: 80.97\% \(\pm\) 7.75\% & \\ [PERSON] [30] & demographic, medical & Specificity: 61.37\% \(\pm\) 14.4\% & Input contains image data \\  & & Non-CAD & from scans \\  & & Sensitivity: 61.37\% & \\  & & Specificity: 80.97\% & \\  & & Accuracy: 89.5\% & \\ [PERSON] [31] & demographic, medical & Sensitivity: 89.8\% & Input contains image data \\  & & Specificity: 88.9\% & from scans \\ [PERSON] [32] & demographic, medical, & Best of all—LSTM & \\  & patient history & Accuracy: 95.34\% & Input contains image data \\  & & AUC: 0.99 & from scans \\ [PERSON] [33] & demographic, medical, & AUC: 0.91 & Input contains image data \\  & doctor’s diagnosis & AUC: 0.91 & from scans and \\  & & doctor-in-the-loop \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work on AI used for CAD classification. for 43.43% of the total cohort, while the remaining participants are considered healthy controls. The subjects in this patient population display notable diversity in their biometric and clinical data. Regarding demographics, approximately 79.68% of the entries correspond to male participants, with ages spanning from 32 to 90 years and body mass index (BMI) values ranging from 16.53 (underweight) to 87.2 (extremely obese). Additionally, various clinical data, such as arterial hypertension and chronic kidney disease, are combined with lifestyle factors like smoking status and diagnostic resting electrocardiogram results. The complete list of 27 features is presented in detail in Table 2.  
For this study, we opted to explain just the best-performing dataset without the expert's diagnosis. Including the expert's diagnosis as an input datum can enhance the accuracy of a prediction model. Nevertheless, the doctor's diagnosis encapsulates all the data from medical tests, images and medical history of a patient and other features. Hence, some features' importance is already represented in the expert's diagnosis. Without a doubt, this is especially true for the most common risk factors for CAD, as they tend to have a greater impact on the decision process of the doctor. Thus, by choosing this approach, even though the prediction accuracy may be somewhat lesser, we believe we can purely capture the decision making process of the algorithm/model and showcase the \"true\" weight the algorithm assigns to each of the input features. In this study, all patients included in the dataset underwent gated-SPECT myocardial perfusion imaging (MPI) as part of the diagnostic process.