## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background  
Healthcare professionals relied on several conventional methods for cardiovascular disease prediction. An example of conventional methods of cardiovascular disease prediction includes clinical risk factors that are related to age, gender, family history, and personal medical history. In addition, echocardiography can be used for visualization of the heart function [20] where electrocardiogram (ECG) can be used to detect signs of congestive  
Figure 1: Structure of the review paper demonstrating the contents of each section and subsection. heart failure [21]. In particular, ECG can help in the prognosis and treatment management of patients diagnosed with congestive heart failure [22]. Cardiac catheterization can also be used to diagnose and evaluate coronary artery disease (CAD) and typical issues with the heart and blood vessels [23; 24]. However, recently, there has been a growing need for more advanced predictive models, such as those powered by machine learning, to improve the accuracy and efficiency of cardiovascular disease prediction [25]. Machine learning is a subset of artificial intelligence (AI) that uses algorithms to allow computer agents to perceive, acquire knowledge, identify patterns, and make intelligent decisions by analyzing collected data [26; 27; 28]. With its ability to evaluate enormous amounts of patient data, machine learning has emerged as a key player for achieving accurate and trustworthy cardiovascular disease prediction [29]. The predictive power of machine learning techniques has emerged as a promising path for revolutionizing the management of cardiovascular disease [17; 30; 31]. Machine learning can enhance early disease detection, accelerate the development of drugs, provide data-driven insights, enable remote monitoring, acquire crucial information from patient's datasets, allow data-driven decision-making, improve image and speech recognition, and simplify administrative procedures [32; 33; 34]. This enables early detection, often before symptoms become severe, allowing for timely intervention and treatment, hence potentially lowering healthcare costs. Analyzing such vast amounts of data in the healthcare field is challenging for humans, if not nearly impossible [35]. Hence, the prevalent use of machine learning proves invaluable in extracting meaningful insights from such extensive datasets. Machine learning algorithms are very beneficial for remote healthcare monitoring [36; 37]. Specifically, patients can receive remote monitoring and consultations, reducing the need for frequent hospital visits and improving access to care, particularly in remote areas [38]. Machine learning algorithms can integrate and analyze a vast amount of patient data from various sources, including medical records and notes, genetic information, and diagnostic tests, to identify subtle patterns, to detect early warning signs and risk factors associated with cardiovascular disease [32; 39; 40; 41]. This would help in providing a comprehensive view of a patient's health and enabling a better understanding of cardiovascular disease risk factors and finding subtle patterns. Examining diverse patient data can flag individuals at risk before symptoms appear, enabling timely intervention and reducing healthcare resource burdens. Machine learning algorithms can analyze medical images and extract the relevant features from medical images, such as X-ray, angiograms and magnetic resonance imaging (MRI), echocardiograms, computed tomography (CT) scans, and clinical records, to identify subtle signs of cardiovascular disease [42; 43; 44; 45; 46; 47]. In the literature, there is a predominant focus on supervised machine learning for cardiovascular disease prediction, due to the availability of labeled datasets, leading us to discuss the supervised approach exclusively. Supervised learning involves an algorithm learning from labeled data, allowing it to predict outcomes for new, unlabeled cases, by generalizing knowledge from the provided available data [48].