## 2 Literature Review
### Machine Learning Approaches  
[PERSON] and [PERSON] [14] underscored the critical significance of early detection and diagnosis of coronary heart disease (CHD), a leading global cause of mortality. To facilitate the training and evaluation of diverse deep neural network (DNN) architectures, including convolutional neural networks and recurrent neural networks, they curated a comprehensive dataset comprising 303 patients and 14 clinical attributes, encompassing factors like age, gender, and cholesterol levels. Their results demonstrated that the proposed DNN models outperformed established methods like logistic regression and decision trees, showcasing high accuracy in CHD detection. Furthermore, feature importance analysis revealed that age, maximum heart rate, and ST segment depression, were the three most critical variables for predicting CHD. [PERSON] and [PERSON] [15] introduced a machine learning framework designed for feature selection in heart disease classification, leveraging an enhanced particle swarm optimization (PSO) algorithm in conjunction with an SVM classifier. The innovative PSO algorithm, crafted with the unique blend of a hybrid mutation operator, velocity clamping, and adaptive inertia weight, aimed to overcome the limitations of conventional PSO methods. Evaluating the framework using the Cleveland heart disease dataset, the results showcased its superiority over alternative feature selection techniques. Notably, the framework exhibited a high degree of accuracy in classifying heart disease, underscoring its potential for improving the accuracy and effectiveness of heart disease diagnosis. [PERSON] et al. [16] presented a study focused on predicting the risk of heart disease by employing advanced machine learning techniques. The research explores innovative approaches to risk assessment in cardiovascular health, utilizing a diverse range of machine learning algorithms. By leveraging extensive data and applying advanced analytics, the study aims to enhance the accuracy and effectiveness of heart disease risk prediction. This work contributes to the field of cardiovascular medicine and underscores the potential of machine learning in improving heart disease risk assessment and patient care. [PERSON] et al. [17] presented HealthFog, a smart healthcare system that used ensemble deep learning techniques for the autonomous diagnosis of cardiac illnesses in an integrated Internet of Things (IoT) and fog computing environment. The system was able to effectively diagnose heart illnesses by processing real-time data from numerous sensors and devices, including blood pressure monitors and electrocardiogram (ECG) devices. The HealthFog system's patient monitoring module, data preprocessing module, feature extraction and selection module, and classification module, were all covered in the authors' full architecture presentation. The findings demonstrated that the HealthFog system performed better than other current systems in terms of precision and timeliness. [PERSON] et al. [18] focused on heart disease prediction through the application of numerous algorithms, including KNNs, LR, and RF. Their research explores the utilization of these algorithms to enhance the accuracy of heart disease risk assessment and prediction. By leveraging advanced data analytics and machine learning techniques, the study aims to contribute to the field of cardiovascular medicine and improve the effectiveness of heart disease prediction, potentially leading to better patient care. [PERSON] et al. [19] reported on a study that used machine learning and statistical analysis to increase the precision of heart disease prediction. They chose the most important candidate features from a list of candidate features using the two statistical models. On the basis of the chosen features, they then applied a support vector machine to create prediction models. According to the findings, the two statistical models and the SVM combination had the highest level of success in predicting heart disease.