# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 1 Introduction
To generalize across multiple contexts, DL-based CAD identification models demand an extensive amount of labeled training data [22]. CNN models typically analyze small portions of a CCTA image and lack an adequate understanding of the underlying context [22]. In significant medical scenarios, uncertainty assessment is crucial. The model's ability to reinforce and magnify biases in the training data raises ethical concerns, specifically when applied to populations with varying levels of diversity [23; 24; 25]. EL combines predictions from models trained on diverse datasets to reduce class imbalances [26]. This helps in avoiding the majority class's dominance in predictions. Interpretability may be improved by combining the outcomes of the simpler and more complex models. Uncertainty is intrinsically measured by ensemble models [26]. The capability of EL-based models to address the limitations and uncertainties of individual models makes them a beneficial technique for healthcare applications. Across different datasets and environments, ensembles frequently deliver higher levels of performance [26]. The ability to offer dependable and consistent outcomes is significant in medical applications. It is possible to reduce the impact of biases in individual models by using ensemble approaches. In order to generate an evenly distributed CAD detection system, it is recommended to use ensembles with diverse biases, as individual models may have a bias toward certain kinds of data [27]. In recent times, researchers have used EL to build effective and reliable medical image classifiers. [PERSON] et al. [26] developed an EL-based CAD detection model using echocardiography. The authors [27] proposed a model based on the EL technique for detecting CAD. [PERSON] et al. [28] proposed a model to detect CAD using CCTA images. In study [29], the authors proposed a CNN-based CAD detection model. [PERSON] et al. [30] built an assessment model using the CCTA images. [PERSON] et al. [31] proposed a vascular extraction and stenosis detection model using the DL technique. [PERSON] et al. [32]employed a DL technique for automating CAD detection. [PERSON] et al. [33] applied a U-Net model for segmenting the coronary arteries from the CCTA images. [PERSON] et al. [34] built a model for predicting cardiovascular risk using a deep CNN model. [PERSON] et al. [35] developed a reporting system in order to predict CAD. They employed pre-trained models to extract the crucial features. [PERSON] et al. [36] employed a CNN model to identify CAD. However, training and maintaining multiple models may be computationally expensive, especially when dealing with massive datasets [37]. The efficiency of EL-based models may be influenced by mislabeled or extremely noisy data. In comparison to individual models, ensemble models demand extensive training periods. There is a lack of a CAD detection model that can operate with limited processing power and storage space. In addition, a resource-constrained CAD detection model can be integrated with modern medical diagnostics and accelerate early identification. Therefore, the authors intend to build an EL-based detection model to detect CAD with limited resources. The contributions of the proposed study are as follows:  
1. A feature engineering model based on the MobileNet V3 model is proposed for extracting meaningful features from the CCTA images. 2. An EL-based CAD detection model is introduced using CatBoost, LightGBM, and RF classifiers to classify the CCTA images into normal and abnormal classes. 3. Generalization of the CAD detection model using real-time datasets. The study is organized as follows: Section 2 presents the proposed methodology for detecting CAD using the CCTA images. The experimental results are presented in Section 3. Section 4 discusses the significance of the proposed CAD detection model. Lastly, the contributions and future directions of the proposed study are presented in Section 5.