## 2 Literature
For evaluating the model, data from 364 patients were included and utilized as an external validation dataset. The ANN generated a better AUC value (0.9), demonstrating high capabilities for future studies. [PERSON] et al., in [27], presented three methodologies for diagnosing ischemic heart disease: SVM, principal component algorithm (PCA), and NN. The results from the three methodologies' implementation were compared against the CLIP3 algorithm, which is a combination of the decision tree algorithm and the rule induction algorithm. A total of 267 SPECT MPI heart images were included, havingpreviously undergone a stress and rest examination. As a result of the complete experiment, the SVM outperformed all and obtained higher accuracy and specificity. Nevertheless, PCA achieved better sensitivity, and the sensitivity of NN was surprisingly low. [PERSON] et al., in [18], presented an explainable DL model for the extraction of the probability of CAD, with SPECT MPI stress and rest polar maps. A total of 3578 patients were included and clinical data such as age, sex, and cardiac volumes were added. Furthermore, attenuation maps were generated with Grad-CAM, which constitutes an explainable tool that ensures the fact that model predictions are accurate and related to the problem. The CNN model demonstrated higher AUC value (0.83), in contrast to an expert's visual analysis (0.71) and a quantitative approach (0.78). The results were evaluated with both 10-fold testing and external testing on unseen data. [PERSON] et al., in [4], examined the utilization of CZT SPECT myocardial perfusion images for the classification of CAD. The authors developed a three-dimensional CNN to classify abnormal and normal patients. In addition, they followed a five-fold cross-validation approach for the hyper-parameter adjustment and experimentation of the model's ability. The process of visualizing the model's decision was applied after using Grad-CAM, which produces heatmaps, highlighting the regions that correspond to the predicted class. The CNN model achieved magnificent results in accuracy, sensitivity, and specificity, such as 87.64%, 81.58%, and 92.16%, respectively. [PERSON] et al. [28] developed a DL model and compared its performance against TPD. A total of 1160 patients, in four different centers, without known CAD were included in raw polar maps in an upright and supine stress MPI demonstration. Besides the images, the following clinical data were added: sex and body mass index (BMI). Grad-CAM was applied to evaluate the CNN. The sensitivity extracted by the DL methodology was higher, in contrast to those of SSS (summed stress score), U-TPD (upright-TPD), and S-TPD (supine-TPD), with values of 82%, 75%, 77%, and 73% in men, and 71%, 71%, 70%, and 65% in women, respectively. [PERSON] et al., in [29], proposed a CNN for automatic CAD analysis. The authors developed a graph CNN for automatically classifying 946 MPI polar maps into normal and abnormal in a stress and rest representation. They further compared the results against three other neural network methodologies. The proposed model extracted similar results to human analysis, which were 89.3% for rest polar maps and 91.1% for stress polar maps. The results were evaluated with the utilization of a four-fold cross-validation procedure. It must be mentioned that heatmaps were generated to visualize the model's decisions behind the classification. [PERSON] et al., in [13], developed an explainable technique, namely layer-wise relevance propagation (LPR), which produces an individual relevance map for each patient to explain the 3D-CNN model concerning the classification of DAT-SPECT data in regards [PERSON]'s disease. A total of 1296 SPECT MPI images were included and classified by experienced readers into normal and abnormal. The extracted results were magnificent in terms of accuracy, sensitivity, and specificity values, which were 95.8%, 92.8%, and 98.7%, respectively.