## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
The work in [147] introduced an approach based on the ensemble Quine McCluskey Binary Classifier (QMBC) to identify patients with cardiovascular disease. A new hybrid machine learning model technique based on the combination of Random Forest and Decision Tree techniques has been proposed in [148]. The results revealed an accurate prediction rate for cardiovascular disease using the hybrid model. In addition, a technique named (CardioHelp) has been introduced in [149], which employs a deep learning CNN approach to estimate the likelihood of cardiovascular disease in a patient. The experimental results demonstrated that the proposed method achieved an accuracy of 97%, which outperforms the existing approaches. Various machine learning algorithms are employed to build predictive models for cardiovascular disease prediction. Some popular algorithms used for this purpose are discussed above. Each one of these algorithms has its unique characteristics, strengths, and weaknesses, making them suitable for different scenarios. Each one of these models leverages distinct algorithms and methodologies, offering unique advantages and insights. From the simplicity of Logistic Regression to the intricacy of deep neural networks, these models have been pivotal in advancing our ability to assess and mitigate the risk of cardiovascular disease [150]. Noting that the choice of algorithm depends on various factors, such as the size of the dataset, the quality of the dataset, and the need for interpretability. Machine learning models, including neural networks, hold the promise of accurately diagnosing coronary cardiovascular disease [151]. Some algorithms, like Decision Trees and Random Forests, are favored when interpretability is crucial. SVMs and KNN can be effective when dealing with complex, high-dimensional data. Logistic Regression and Naive Bayes are simple and suitable for binary classification tasks. It is also worth noting that ensemble methods, like Random Forests, are often preferred in cardiovascular disease prediction to combine the strengths of multiple algorithms and improve overall model performance. The choice of algorithm should be made based on a thorough understanding of the data and the specific goals of the cardiovascular disease prediction task. Recent results developed by [12], indicated that deep learning algorithm surpasses the capabilities of traditional machine learning methods, especially when it comes to handling large datasets, enabling the simultaneous training of substantial amounts of data for high prediction accuracy. The deep learning method consistently delivers accuracy levels of 85%. Moreover, combining various techniques within a single model is expected to yield promising forecasting results. For instance, machine learning models employing supplementary techniques like feature selection can achieve a maximum accuracy of 97% as stated in [12]. Table 14 provides comparisons between different algorithms with different datasets that are used for cardiovascular disease prediction. The comparison is carried out in terms of accuracy, precision, recall, and F1-score.