## 1 Introduction
### Related Work  
In recent years, significant progress has been made in applying data mining and ML methods to predict CVDs, emphasizing early detection and prevention. These advancements have been strongly influenced by seminal studies in the field, prompting an examination of their methodologies, outcomes, and limitations. Researchers have demonstrated the potential of various ML and deep learning models to achieve high accuracy in predicting CVDs. The authors in [15], from the very outset in 2017, evaluated four different models using clinical data from over 300,000 homes in the United Kingdom, with neural networks demonstrating the highest accuracy in predicting CVDs, particularly with extensive datasets. Another research [16] developed an ensemble of ML and deep learning models that achieved an 88.70% accuracy, with the ML ensemble model emerging as the most accurate. Another study [17] designed a machine intelligent framework (MIFH) for predicting heart diseases, achieving improved sensitivity rates and selecting the best possible solution among input predictive models. In [18], the authors proposed a model that integrates bagging ensemble learning with decision trees and employs feature extraction via Principal Component Analysis (PCA), achieving an impressive accuracy rate of 98.6% on a practical heart dataset. The researchers in [19] developed a predictive model aimed at forecasting the likelihood of cardiac events in hypertensive individuals, utilizing electrocardiogram (ECG) data as the input. They creatively combined a convolutional neural network with a long short-term memory network, resulting in a hybrid model. This fusion leveraged time-series data to detect early increases in hypertension occurrences among individuals. The research in [20] directed efforts towards creating an intelligent agent for CVD prediction using a multi-layer dynamic system based on ensembles. The proposed model integrates five feature selection algorithms alongside advanced ensemble learning techniques, achieving high accuracy rates, with up to a 94.16% accuracy and a 0.94 Area Under the Curve (AUC) value on a realistic heart dataset. Notably, this multi-layer dynamic system perpetuates the classification process across successive layers by enhancing knowledge at each level, yielding optimal results. The study in [21] surpassed other ML algorithms discussed in the literature, achieving a 96.7% accuracy using the random forest classifier and 95.08% using extreme gradient boosting on the Cleveland dataset in 2023. Another study [22] attained an 86% AUC score in predicting diabetes mellitus using the CatBoost classifier. Alongside SHAP values, they identified glycose levels and age as the most influential features. A recent development in [23] introduced an advanced hybrid ensemble gain ratio feature selection (AHEG-FS) model. This model concentrates on enhancing accuracy and AUC by selecting highly effective features while retaining relevant ones. Nine ML classifiers--adaBoost, logistic regression (LR), classification via clustering (CVC), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), boosted regression tree (BRT), naive Bayes (NB), and stochastic gradient boosting (SGB)--were integrated with the proposed AHEG-FS model, which was tailored on medical datasets to design an innovative methodology for enhancing prediction performance. The model achieved an impressive 99% AUC after reducing features by 46.15%. According to another research [24], methods like CatBoost, random forest, and gradient boosting can accurately predict almost eight out of ten cardiac arrests. Additionally, the authors in [25] achieved an impressive 98.15% accuracy on a Kaggle dataset, showcasing the efficacy of ensemble methods like the extra tree classifier in predicting heart diseases. The research in [26] proposed the further application of deep neural networks to address heart disease diagnosis, achieving a 90% accuracy rate on the Cleveland dataset by utilizing Talos for optimizing hyperparameters.