## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to minimize the number of features while retaining most of the variance [65; 66]. For example, dimensionality reduction using the PCA technique has been applied in [67; 68; 69] for cardiovascular disease detection. Other techniques such as firefly algorithm [70] and minimum redundancy maximum relevance [71] have also been applied for dimensionality reduction in cardiovascular disease prediction. Applying such efficient dimensionality reduction techniques can improve model efficiency and interpretability. Therefore feature engineering with dimensionality reduction has the ability to improve the data selection, hence improving prediction accuracy [72]. Furthermore, a model with too many features relative to the number of instances in the dataset is at risk of overfitting, where it fits the noise rather than the underlying patterns [73]. Feature selection can mitigate this overfitting risk by simplifying the model and reducing its complexity. Feature selection methods are categorized into filter and wrapper methods [74]. The wrapper methods choose an optimal subset of features by incorporating the classifier, whereas the filter methods select features independently of the classifier. The most common feature selection methods are univariate, weighted least square, rough sets, fast correlation-based filter (FCBF), and sequential forward selection [5; 75; 76; 77; 78; 79]. The work in [62] has proposed three feature selection algorithms, which are Relief, minimal-redundancy-maximal-relevance (mRMR), and least absolute shrinkage and selection opera tor (LASSO) to identify the most crucial and strongly correlated features that significantly impact the prediction of cardiovascular disease. Univariate and Relief feature selection methods were proposed in [5], where the univariate method utilizes a statistical approach to select a subset of features that has the strongest relationship with a class label. In contrast, the Relief technique gives each feature in the dataset a weight, and these weights are modified over time. In [80], a new approach known as a hybrid random forest with a linear model (HRFLM) is introduced. This method focuses on identifying important features using machine learning techniques, hence, leading to improved accuracy in cardiovascular disease prediction. The prediction model incorporates various feature combinations and employs several established classification methods. As a result, the proposed approach enhances the performance of cardiovascular disease prediction. Recently, evolutionary methods have emerged as a significant class of techniques that can be utilized efficiently for feature selection and prediction of cardiovascular diseases. For example, the work in [81] focused on involving the identification and selection of crucial features, along with the exploration of machine learning techniques, to augment the predictive capacity of classification models for accurately predicting cardiovascular disease. To this end, a hybrid ensemble model using genetic algorithm (GA) and linear discriminant analysis (LDA) was proposed to improve the prediction accuracy. The work in [82] proposed a combination of convolutional neural network (CNN) and jellyfish search optimizer (jSO) approach for the prediction of cardiovascular diseases. In particular, the jSO optimization algorithm is exploited to tune the CNN hyperparameters and improve the accuracy. The work in [83] introduced a new model named hyOPTXg, which was designed for predicting cardiovascular disease through an optimized XGBoost classifier. Consequently, fine-tuned hyperparameters of XGBoost and conducted model training using the optimized parameters were proposed to achieve a superior performance enhancement in cardiovascular disease prediction.