## 1 Introduction
### Related Work
The study in [27] utilized an artificial neural network (ANN) on the same dataset and assessed its performance across different learning rates and numbers of neurons. The results indicated that the highest accuracy of 80.6% was attained with a learning rate of 0.25 and 25 neurons. In another study [28], a deep learning methodology was recommended, coupled with the isolation forest algorithm for feature extraction, resulting in an improved accuracy of 94.2% for the University of California Irvine (UCI) dataset. [29] subsequently developed a stacking model comprising both a base learner layer and a meta-learner layer, achieving an impressive accuracy of nearly 96% for predicting the presence or absence of heart disease. These noteworthy findings underscore the potential of deep learning approaches in enhancing predictive performance. However, the aforementioned studies on heart disease prediction primarily utilize small and relatively balanced datasets. To address the challenge of dataset imbalance in a large-scale dataset, we aim to determine the most suitable classification algorithms for heart disease prediction. Authors in [30] employed a novel approach by integrating stacking ensemble modelling, which combines SVM, NB, and KNN with a 10-fold cross-validation Synthetic Minority Oversampling Technique (SMOTE). This method effectively handles softly imbalanced datasets, yielding a robust accuracy of 90.9%. Researchers in [31] utilized the Synthetic Minority Oversampling Technique along with the edited nearest neighbor (SMOTE-ENN) data resampling technique and hyperparameter optimization, resulting in notable improvements in classifier performance, especially in predicting the survival of patients with heart failure. Another study in [32] also leveraged the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and noise present in the Cleveland dataset. By employing an AdaBoost random forest classifier, they achieved an accuracy of 95.47% in the early detection of heart disease. Researchers in [33] attempted to mitigate imbalance in the National Health & Nutrition Examination Survey (NHANES) dataset through a two-step approach involving a least absolute shrinkage and selection operator (LASSO)-based feature weight assessment, followed by majority-voting-based identification of important features. The authors in [34] chose to under sample cases without CVDs to reduce medical resource consumption and false positive cases. They developed a three-layered model that iteratively trains models and incorporates predictions from previous layers as features. Also, in their investigation, [35] achieved a notable 30.4% improvement in their model's sensitivity by integrating the SMOTE-Tomek with random forest algorithm. Another research [2] also utilized the 2021 BRPSS data, which mirrors the dataset used in our study, advocated for increased involvement of logistic regression in the workflow for CVD prediction. They demonstrated that logistic regression accurately classified 79.18% of individuals with CVDs and 73.46% of healthy individuals, identifying sex, diabetes, and general health as the most influential factors in predictions. These findings align well with our study, in which we offer improved results, and we also emphasize minimizing false negatives to help ensure timely medical attention for high-risk individuals.