## 1 Introduction  
The heart is a vital organ responsible for circulating blood throughout the body, which is crucial for maintaining overall health and well-being [1]. Impaired heart function can harm other organs, including the kidneys and brain, emphasizing the importance of maintaining a healthy cardiovascular system [2]. Heart disease is a prevalent and severe condition worldwide that contributes to millions of deaths every year, making it a significant global health concern [3, 4]. Notably, certain regions, such as Asia and the United States, bear a higher burden of heart disease [5]. Moreover, gender and age also influence the risk of developing heart issues, with males and older individuals being more susceptible [6].  
Extensive medical research has identified various risk factors associated with heart disease, including physical inactivity, poor lifestyle choices, obesity, and unhealthy diet. Additionally, certain conditions, such as high blood pressure, smoking, family history, hypertension, stress, diabetes, and high cholesterol, can further elevate the risk of heart disease [7, 8, 9, 10, 11, 12]. Given the complexity and multifactorial nature of heart disease, effective prediction and prevention strategies are essential for mitigating its impact.  
Data mining and machine learning techniques have emerged as valuable tools for analyzing large datasets and extracting valuable insights from across numerous domains, including healthcare [13]. In particular, machine learning algorithms can effectively process vast amounts of medical data to identify hidden patterns and facilitate accurate disease diagnosis, detection, and prediction [14, 15, 16]. Despite previous research efforts in disease prediction, achieving reliable outcomes in heart disease prediction remains a challenge [17]. To address this gap, this study presents a prediction model for heart disease that combines ensemble learning algorithms, hyperparameter optimization techniques, and preprocessing steps. The model aims to accurately predict heart disease by evaluating the classification accuracy of various ensemble learning algorithms, including random forest, extra tree classifier, XGBoost, and CatBoost. Hyperparameter optimization techniques, such as grid search CV and randomized search CV, are employed to find the optimal hyperparameters for these algorithms. Additionally, preprocessing steps, such as handling missing values, removing duplicates and outliers, and normalizing data, are applied to enhance the model's performance.  
The main contributions of this work are as follows:  
1. A predictive model that focuses on hyperparameter tuning through hyperparameter optimization techniques and preprocessing steps;
2. The improved accuracy of heart disease prediction, reductions in false predictions, and a comparison of the default and optimal hyperparameters using grid search CV and randomized search CV to determine the optimal approach;
3. The identification of the most suitable prediction algorithm among four ensemble learning algorithms for accurately classifying a combined heart disease dataset;
4. The validation of the model's performance using various metrics, such as accuracy, precision, recall, F1-score, Cohen's kappa, precision-recall curve (PRC), and the area under the ROC curve, with the experimental results demonstrating the superiority of the proposed model.