## 2 Literature Review  
In recent years, there has been significant progress in using data mining and machine learning techniques for predicting heart disease, focusing on early diagnosis and prevention. Several key studies within the field have contributed to this advancement. Here, we discuss these studies, their methodologies, findings, and limitations. In 2023, [PERSON] et al. [18] conducted a study that applied various machine learning algorithms, including random forest, decision tree, multilayer perceptron (MLP), and XGBoost. Their dataset consisted of approximately 59,000 rows and 11 attributes. The MLP algorithm yielded the highest cross-validation accuracy of 87.28%, demonstrating excellent precision, recall, F1-score, and area under the ROC curve values. Similarly, in 2023, [PERSON] and [PERSON] [19] conducted a predictive analysis of heart disease risk factors using two independent datasets. They trained classification models on the Cleveland dataset and validated the results using the Statlog dataset. Through exploratory analysis, they identified significant associations between specific bio-clinical variables and heart disease. The support vector machine (SVM) exhibited the best performance among the 10 classification models trained on the Cleveland dataset, achieving 85% accuracy, 82% sensitivity, 88% specificity, 87% precision, and 38% log loss. The findings were validated using 10-fold CV on the Statlog dataset, demonstrating a consistent performance. In another study conducted in 2023 by [PERSON] [20], decision tree, KNN, SVM, and XGBoost algorithms were employed to predict heart disease based on 11 clinical features. The SVM algorithm outperformed the other models, achieving 88.8% accuracy, 89.3% recall, and 90.7% f1-score. The study emphasized the significance of angina and ST depression induced by exercise as important features associated with heart disease. Additionally, in 2023, [PERSON] et al. [21] analyzed a dataset of randomly selected patients with heart disease using various machine learning algorithms, including decision tree, SVM, naive Bayes, logistic regression, and random forest. The random forest algorithm demonstrated the highest accuracy of 85.01% and outperformed the other algorithms, highlighting the potential of machine learning for analyzing and predicting cardiovascular disease. Other studies have also contributed to predicting heart disease using machine learning techniques. For example, [PERSON] et al. (2023) found that logistic regression achieved the best results for heart disease prediction, with an accuracy of 96.7%, using a 70:30 split ratio and the robust scaling method [22]. Additionally, [PERSON]. (2020) found that KNN achieved the best results for heart disease prediction, with an accuracy of 99.7073% [23]. [PERSON] et al. (2020) utilized decision tree, naive Bayes, KNN, and random forest models on the Cleveland dataset and found that KNN provided the most reliable prediction, with an accuracy of 86.885% [24]. Similarly, [PERSON] et al. (2021) compared the performance of KNN and random forest models using the Kaggle heart disease dataset, with KNN achieving the higher accuracy of 86.885% [25]. [PERSON] (2021) conducted feature analysis and employed the LASSO algorithm for feature selection to predict cardiac disease. Traditional classifiers were utilized, including decision trees and neural networks, with the dense neural network achieving the highest testing accuracy of 73.9% and an F1-score of 72.0% [26]. These studies have made significant progress in predicting heart disease using machine learning models. However, they have also encountered limitations, such as small sample sizes and methodological variations, which have affected the accuracy and reliability of their predictions. One important aspect that has been overlooked in these studies is the utilization of ensemble learning techniques with hyperparameter optimization for heart disease prediction.