# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 3 Results  
To evaluate the performance of the proposed CAD detection model, the authors used Windows 10 Professional, Intel i7 with 16 GB RAM, NVIDIA GeForce RTX 3050, and the Python 3.8.1 environment. They generalized the model using datasets 1 and 2. The datasets are divided into a train set (70%) and a test set (30%). The source codes of MobileNet V3, CatBoost, and LightGBM are extracted from the Github repositories [39; 40]. However, the suggested image pre-processing and fine-tuning procedures are required to generate the outcome. In addition, PyTorch and Keras libraries are used for the model development. The authors trained the model with epochs of 12 and 16 and batches of 14 and 15 for datasets 1 and 2, respectively. A learning rate of \(1\times 10^{-4}\), strides of 2, a decay rate of 0.94 per 2 epochs, and a sigmoid function are used for the image classification. Figure 5 shows the performance of the proposed model in different epochs. The proposed EL-based CAD detection achieved an exceptional outcome. The suggested feature engineering model assists the model in identifying the normal and abnormal classes effectively. The binary classification performance of the proposed model is listed in Table 2. Figure 6 highlights the significance of the recommended CAD detection model. It is evident that the proposed preprocess techniques and feature engineering model have addressed the existing challenges in generating meaningful features. Figure 5: Performance analysis at different epochs. The findings of the comparative analysis using dataset 1 are presented in Table 3. The recommended model outperforms the existing CAD detection by achieving superior results. The suggested stacked generalization using CatBoost, LightGBM, and RF models produced remarkable performance. Figure 7 reveals the performance of the CAD detection models on dataset 1. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classes/Metrics** & **Acc** & **Pre** & **Rec** & **F1** & **Kap** \\ \hline  & & Dataset 1 & & & \\ \hline Normal & 99.8 & 98.9 & 99.1 & 99.0 & 95.6 \\ \hline Abnormal & 99.7 & 99.1 & 98.8 & 98.9 & 95.8 \\ \hline Average & 99.7 & 99.0 & 98.9 & 98.9 & 95.7 \\ \hline  & & Dataset 2 & & & \\ \hline Normal & 99.6 & 98.8 & 98.7 & 98.7 & 95.7 \\ \hline Abnormal & 99.7 & 98.9 & 98.6 & 98.7 & 95.8 \\ \hline Average & 99.6 & 98.8 & 98.6 & 98.7 & 95.7 \\ \hline \end{tabular}
\end{table} 
Table 2: Findings of performance analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classes/Metrics** & **Acc** & **Pre** & **Rec** & **F1** & **Kap** \\ \hline [PERSON] et al. [28] & 98.6 & 98.2 & 98.1 & 98.1 & 95.1 \\ \hline [PERSON] et al. [29] & 99.5 & 98.9 & 98.9 & 98.9 & 96.2 \\ \hline [PERSON] et al. [35] & 98.3 & 97.2 & 97.0 & 97.1 & 94.1 \\ \hline [PERSON] et al. [37] & 97.4 & 97.5 & 97.8 & 97.6 & 93.4 \\ \hline [PERSON] et al. [36] & 98.5 & 97.9 & 97.6 & 97.7 & 94.7 \\ \hline EfficientNet B7 & 97.8 & 96.9 & 97.2 & 97.0 & 91.5 \\ \hline MobileNet V3 & 98.1 & 97.4 & 97.6 & 97.5 & 92.1 \\ \hline Proposed Model & 99.7 & 99.0 & 98.9 & 98.9 & 95.7 \\ \hline \end{tabular}
\end{table} 
Table 3: Outcome of comparative analysis—dataset 1. Figure 6: Findings of performance analysis. Table 4 offers the outcome of the comparative analysis using dataset 2. The recommended hyperparameter optimization has tuned the RF classifier to yield an effective outcome. In addition, the feature engineering technique generated meaningful features from the CCTA images. The results of the comparative analysis are presented in Figure 8.