## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
In [124], prediction models for coronary cardiovascular disease (CHD), which is known as cardiovascular disease have been proposed. To this end, various supervised machine learning algorithms, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Random Forest, are employed in cardiovascular disease prediction. The results demonstrated that the Gaussian Naive Bayes, Bernoulli Naive Bayes, and Random Forest algorithms achieved accuracy rates of 85%, 85%, and 75%, respectively. The work in [125] proposed an ensemble model adopted to enhance predictive accuracy by combining the strengths of multiple classifiers. To this end, ensemble learning is employed, amalgamating five classifier models--SVM, ANN, Naive Bayes, regression analysis, and random forest--to predict and diagnose cardiovascular disease. The work in [126] aimed to explore the utilization of machine learning in predicting cardiovascular attacks based on historical health records of patients. The study concentrated on the application of the random forest and CNN for prediction purposes. The results suggested that random forest exhibited superior performance compared to other classifiers, particularly in terms of classification accuracy. In [127], an SVM based on a decision tree system has been proposed for cardiovascular disease prediction. The results demonstrated that the proposed approach acquired knowledge of decision boundaries with various orientations, thereby demonstrating greater flexibility in learning diverse datasets. In [128] a back-propagation neural network and Logistic Regression algorithms have been proposed for cardiovascular disease prediction. The results showed that the back-propagation neural network and Logistic Regression algorithms achieved an accuracy of 85.074% and 92.58%, respectively. The work in [129] encompasses two domains: signal processing and statistical learning. Leveraging signal processing techniques, authors have successfully segmented and represented each heartbeat through a vector of distinctive characteristics. Then, a cardiac electrocardiogram signal (ECG) is used, which is collected by sensors and proposed an SVM and Cuckoo search-optimized neural network algorithms for automatic cardiovascular disease detection. The work in [130] developed a smart scoring system and investigated the use of SVM for cardiac arrest prediction. The results showed that the intelligent scoring system has proven its capability to produce risk scores that are comprehensible to humans and has exhibited efficacy as a robust predictor of cardiac arrest occurring within a 72-hour timeframe. The work in [131] proposed a CNN with a depth of 9 layers to autonomously recognize five distinct categories of heartbeats, which are ventricular ectopic, supraventricular ectopic, unknown beats, non-ectopic, and fusion, within ECG signals. The results demonstrated that after training the CNN with augmented data, it achieved an accuracy of 94.03% and 93.47% in classifying heartbeats diagnostically in both original and noise-free ECGs, respectively. The work in [132] introduced an innovative method for heartbeat recognition, employing a principal component analysis network for feature extraction from noisy ECG signals. To this end, an SVM algorithm is proposed. The work in [133] proposed KNN and decision tree algorithms for cardiovascular disease diagnosis. The work in [134] proposed a Logistic Regression technique for predicting cardiovascular disease within a cardiovascular dataset by employing dimensionality reduction, aiming to enhance the accuracy of the prediction. To this end, a feature scaling approach is to make the essential features fit with Logistic Regression to analyze the performance. In [65], the ANN algorithm and PCA-based technique are explored to improve the accuracy of cardiovascular disease prediction. The results showed that the accuracy has been improved achieving 97.7%. In [135], the prediction of cardiovascular disease through data mining methods is examined.