## 2 The Role of Machine Learning in Cardiovascular Disease Prediction
### Background
The work in [84] provided a comparative investigation that integrates machine learning algorithms with meta-heuristic algorithms for feature selection, aiming to enhance the classification capabilities of machine learning algorithms by identifying features that significantly influence accuracy. The findings affirm that the amalgamation of machine learning and meta-heuristic algorithms leads to superior classification accuracy with a reduced number of features. Hybrid methodologies that integrate hyper-parameter optimization algorithms with two highly effective classification techniques namely: Support Vector Machines (SVMs) and Long Short-Term Memory (LSTM) neural networks have been proposed in [85] to further improve the accuracy of cardiovascular disease diagnosing. The results were achieved based on the Cleveland dataset and its extension Statlog. The work in [86] proposed a multiobjective approach with a fuzzy system for the classification of cardiovascular risk. The proposed approach involved addressing computational elements such as configuring the fuzzy system, optimizing the process, selecting an appropriate solution from the optimal Pareto front, and ensuring the interpretability of the fuzzy logic system post-optimization. Leveraging multiobjective optimization and Pareto dominance allows the acquisition of a set of optimal solutions that embody the most effective equilibrium between two optimization objectives. The work in [87] focused on creating and automating a disease prediction model to facilitate early detection of cardiovascular disease and its associated risk factors. To this end, feature selection was executed using non-linear Particle Swarm Optimization (NL-PSO). Subsequently, classification was carried out using the Improved Deep Evolutionary model with Feed Forward Neural Networks (IDEBDFN). The algorithm's learning nature was leveraged to assess the characteristics of the hidden layers, ensuring optimal results. The findings illustrate that the proposed model exhibits superior predictive accuracy. The work in [88] introduced an alternative training technique for a multilayer perceptron (MLP) that incorporates a particle swarm optimization (PSO) algorithm for cardiovascular disease detection. The results demonstrated that the proposed hybrid MLP-PSO classifier empowers practitioners to diagnose cardiovascular disease earlier, with enhanced accuracy and efficacy. An approach involving a radial basis function neural network (RBFNN) was proposed in [89], which was coupled with a robust hybrid particle swarm optimization (HPSO). The HPSO incorporated a spiral-shaped mechanism (HPSO-SSM) to enhance the PSO algorithm performance by addressing constraints such as slow convergence and the local minimum challenge. The work in [90] proposed evolutionary algorithms based on Genetic Algorithm (GA) and PSO for the feature selection to further improve the accuracy of machine learning algorithms. The results demonstrated that the feature selection based on GA achieved the best prediction accuracy. Several research papers have also found that genetic algorithm (GA) is a highly effective method for feature selection, see e.g., [91; 92; 93; 94; 95]. Overall, as the development of machine learning continues to shape the future of healthcare, the feature selection approach remains an essential component to improve cardiovascular disease prediction. Machine Learning Models for Cardiovascular Disease Prediction and Related State-of-the-Art Research  
This section provides an exploration of machine learning models designed for the prediction of cardiovascular diseases, accompanied by an in-depth discussion of the state-of-the-art research. In particular, this section comprehensively addresses the advancements, methodologies, and key findings related to cardiovascular disease prediction using machine learning models. Advanced machine learning models are used to analyze heterogeneous data that come from various sources [96].