## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
The choice of machine learning model can significantly impact the quality and trustworthiness of cardiovascular disease prediction [31]. With the abundance of medical data and the ever-increasing computational capabilities, machine learning models have become indispensable [97]. Predictive analytics models are used to stratify patients into different risk categories, enabling targeted interventions. These models can help in identifying individuals at high risk of cardiovascular disease and enable personalized treatment plans. Machine learning models are being integrated into clinical workflows to provide real-time decision support to healthcare professionals [31]. In this subsection, we will explore a range of machine-learning models that are commonly employed for cardiovascular disease prediction. The common machine learning models are Logistic Regression [98], Decision Trees [99; 100; 101], Random Forests [102], support vector machines (SVM) [103; 104], K-Nearest Neighbors (KNN), Adaptive Boosting, commonly referred to as AdaBoost [105; 106], Naive Bayes [107; 108], and Conventional Neural Network (CNN) or what know as deep learning [13]. Table 2 provides a summary of the machine learning models in the prediction of cardiovascular diseases along with the strengths and weaknesses points. To this end, several research works have been done so far to investigate the application of machine learning models in cardiovascular disease prediction. For example, the work in [109] constructed a cardiovascular disease classification system using a Logistic Regression classifier machine learning techniques, which achieves an accuracy of 77%. Cleveland dataset is employed, along with global evolutionary and feature selection methods. The work in [110] designed a diagnostic system for cardiovascular disease classification, utilizing multi-layer perception and SVM algorithms, achieving an accuracy rate of 80.41%. The work [111] introduced a model to determine the most effective machine learning algorithm for early-stage prediction of cardiovascular disease, ensuring high accuracy. The results showed that the best accuracy for cardiovascular disease classification has been achieved using a random forest algorithm with a rate of 95.4%. The work in [112] developed a cardiovascular disease classification system by integrating a neural network with Fuzzy logic, resulting in an accuracy of 87.4%. The work in [113] introduced an ANN ensemble-based diagnostic system for cardiovascular disease, coupled with the statistical measurement system Enterprise Miner. Their system yielded an accuracy of 89.01%, a sensitivity of 80.09%, and a specificity of 95.91%. The work in [114] developed a machine learning-based cardiovascular disease diagnosis system, incorporating the ANN algorithm and a feature selection algorithm, achieving commendable performance. The work in [115] proposed an expert medical diagnosis system for cardiovascular disease identification, using predictive machine learning models like Naive Bayes, Decision Tree, and ANN. These models attained an accuracy of 86.12%, 88.12%, and 80.4%, respectively.