## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
A thorough evaluation of various techniques has been conducted, including the KNN algorithm, decision tree algorithm, neural network classifications, and Bayesian classification algorithms. Additionally, the utility of GA for feature selection is investigated to identify crucial cardiovascular disease-related features. The experiments revealed that the decision tree model achieved a high level of accuracy. The work in [136] utilized an unsupervised clustering method to assess cardiac involvement in systemic sclerosis, revealing unknown connections between samples for cardiovascular disease prediction. The work recommended employing big data methods such as HDFS and SVM for optimal attribute detection. The study also explored various data mining algorithms for cardiovascular disease detection, suggesting storing extensive data across nodes with HDFS and implementing the prediction algorithm using SVM across multiple nodes simultaneously. In [137], the application of a backpropagation neural network for predicting cardiovascular disease is explored. The study involved the use of a deep-learning model in disease prediction. The work employed a neural network for both learning and prediction, utilizing the Cleveland dataset. To enable a real-time diagnosis approach, the work in [40] proposed an integration of a deep learning algorithm into the 6G-enabled Internet of Medical Things (IoMT) to facilitate online sharing of medical records and monitoring results. The work in [138] investigated the prediction of cardiovascular disease using various machine-learning techniques. The study involved the application of classification and regression models, specifically the Decision tree, KNN algorithm, SVM, and linear regression. Experimental results demonstrated that the KNN algorithm achieved the highest level of accuracy in cardiovascular disease prediction. In [139], five different machine learning algorithms for cardiovascular disease prediction have been employed, which are Logistic Regression, KNN, SVM, Decision tree, and Random Forest. To this end, a new hyperparameter tuning model has been proposed and compared with conventional approaches. The experimental results demonstrated that the proposed prediction approach achieved high accuracy in making predictions related to cardiovascular disease for the considered scenario. The work in [140] investigated cardiovascular disease prediction using four different datasets: Cleveland, StatLog, Hungarian, and Z-Alizadeh Sani. To this end, a two-tier ensemble PSObased feature selection was proposed. In [141] two supervised machine learning algorithms, which are KNN and Random Forest, were exploited to predict cardiovascular disease prediction. Besides, KNN, decision trees, and random forests were investigated in [142]. A new approach was proposed in [143] to predict cardiovascular disease, utilizing a hybrid technique that combines a decision tree and an artificial neural network. To this end, a data mining-based approach in [144] was employed to explore, analyze, and extract data from various fields, unveiling meaningful information. In other words, raw data is transformed into valuable and useful information. An approach based on Naive Bayes and decision tree algorithms have been implemented in [145] to predict cardiovascular disease, aiming for a concise diagnosis and analysis using a minimal set of attributes. Researchers frequently employ diverse feature selection techniques in conjunction with machine learning models for cardiovascular disease diagnosis. For example, in [95], the combination of SVM and an AI method was utilized to identify crucial features in classifying cardiovascular disease. The genetic algorithm serves as the feature selection technique, complementing the SVM, which functions as the classification algorithm. A cardiovascular disease diagnosis system was proposed in [146] using a fusion of rough sets-based attribute reduction and fuzzy logic.