## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
In particular, the supervised learning algorithm learns to map input data to a specific output, or target variable, based on a labeled dataset. In supervised learning, the algorithm aims to generalize patterns from the training data in order to perform predictions or classifications of new data. As described in [49], supervised machine learning entails having a predefined output attribute and utilizing input attributes. Supervised algorithms initially perform analytical tasks using training data and then build functions to map new instances of the attribute [50]. Classification and regression algorithms are two categories of supervised machine learning algorithms according to [51; 52]. Labeled data, or what is known as training set [53], is crucial in supervised learning because it provides the algorithm with the ground truth information that is needed to learn and make predictions. Labeled data consists of input features (independent variables) [54; 55] and their corresponding correct target values (dependent variable) or labels [56]. The algorithm uses these labeled examples to identify patterns, associations, and relationships within the data, allowing it to learn how to make predictions on new, unlabeled data. The supervised learning algorithm uses the features to make predictions or classifications about the target variable based on patterns learned from the labeled examples in the dataset [57]. This allows for the creation of predictive models that can help in identifying individuals at risk of cardiovascular disease based on their characteristics. Appendix A provides the attributes of three common datasets for cardiovascular disease prediction, which are the Cardiovascular, Cleveland, and Framingham datasets. Machine learning classification algorithms have the potential to identify patients at risk of cardiovascular disease based on their medical data records, allowing for early medication and treatment. Classification algorithms can categorize patients into risk groups, enabling healthcare providers to prioritize patients at higher risk. Classification algorithms can assist in triaging patients, ensuring that those with the most urgent cardiac issues receive immediate treatments. Therefore, machine learning classification algorithms are essential for cardiovascular disease management, helping efficiently in early disease detection, risk assessment, personalized treatment, and more accurate diagnostics. In summary, machine learning is invaluable in healthcare, especially in the prediction and management of cardiovascular disease. It empowers healthcare providers with the tools to make more informed decisions, enhances patient outcomes, and advances the efficiency of the healthcare system. Significance of Feature Selection in Cardiovascular Disease Prediction and Related State-of-the-Art Research  
In healthcare systems, features are considered as input variables that describe the characteristics of patients [58]. Each individual (patient) in the dataset is represented by a set of feature values [29; 59]. In particular, healthcare datasets may contain irrelevant features that may introduce noise into the model, hence leading to decreased prediction accuracy [60]. Hence, feature selection approaches aim to reduce the input variables by removing redundant or irrelevant features and selecting the most informative and relevant features [61]. To this end, feature selection can be used to improve prediction accuracy and efficiency in healthcare systems [57]. It is crucial in the development of accurate and interpretable predictive models for cardiovascular disease [17]. Additionally, feature selection enhances the classification accuracy and minimizes the model execution time [62]. However, feature selection requires a precise selection of relevant variables from a large set of possible features [63; 64]. Furthermore, feature selection techniques can reduce the dimensionality of the datasets which can be achieved by ignoring the noisy features, and hence, the predictive models can be more accurate.