# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 2 Related Work  
Several ML models have been proposed for heart disease prediction. The authors of [9; 10] employed ensemble methods, incorporating classifiers like Naive Bayes, Decision Tree, Logistic Regression, and Random Forest to enhance prediction accuracy. Also, a hybrid model combining Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, and Decision Tree classifiers to improve accuracy and robustness was introduced in [11]. In a recent study [12], five different ML models were compared using a standard cardiovascular disease dataset. Among these models, bootstrap aggregation yielded the highest accuracy of 97.67%. Another noteworthy work focused on explainable AI [13] for cardiovascular disease prediction; the authors employed various traditional ML algorithms to enhance the models' interpretability. [PERSON] et al. [14] introduced a comprehensive taxonomy, providing a detailed categorization based on the fundamental attributes exhibited in these approaches. Additionally, their extensive investigation delved into the classification performance across various datasets, elucidating the efficacy of these methodologies in a rigorous manner. In [15], the researchers introduce a novel semi-supervised learning algorithm founded on the principles of self-training. This algorithm employs multiple independent base learners initially and dynamically identifies the most promising base learner during the training phase using a strategy based on the number of highly confident predictions from unlabeled data. The research studies in [16; 17] explored the integration of various classifiers into their heart disease prediction models, including Random Forest, Support Vector Machine, Naive Bayes, and Decision Trees. In both studies, the Support Vector Machine (SVM) demonstrated the best performance in terms of accuracy and predictive capabilities. Furthermore, refs. [18; 19] focused on feature selection and evaluation. Specifically, ref. [18] used Random Forest in combination with Logistic Regression to assess the importance of features, while [19] employed multiple classifiers, including K-Nearest Neighbors, Decision Tree, Logistic Regression, Naive Bayes, SVM, and Random Forest, to improve accuracy and performance. A comprehensive analysis utilizing Logistic Regression, LightGBM, XGBoost, Gaussian Naive Bayes, SVM, and Gaussian Naive Bayes, achieving varying accuracies, was presented in [20]. Another ensemble model combining logistic regression and a majority voting approach, which outperformed existing methods for heart disease prediction, was proposed in [21]. In another work [22], Random Forest, Neural Network, Decision Tree, and SVM were combined in a hybrid model for heart disease prediction. The authors of [23] focused on using Deep Neural Network and S2 statistics to predict heart disease while addressing overfitting and underfitting issues. The authors of [24] achieved higher accuracy by working on an Artificial Neural Network for cardiovascular disease prediction. Decision Tree, Random Forest, and Naive Bayes classifiers were used in [25] for disease prediction. A hybrid approach combining Random Forest and Linear Model for heart disease prediction was utilized in [26]. In addition, an estimation model using bagging techniques with base learners including Naive Bayes, K-Nearest Neighbors, and Logistic Regression, was proposed in [27]. A detailed analysis of different ML algorithms for predicting cardiovascular disease, includingRandom Forest, Decision Tree, Logistic Regression, SVM, and K-Nearest Neighbors, was conducted in [28]. A model that utilized SVM and Artificial Neural Network (ANN) was also presented in [29]. Moreover, an intelligent model utilizing Random Forest for disease prediction, focusing on the importance of smart disease prediction within a ML framework, was introduced in [30]. Finally, authors the authors of [31] proposed a model using Logistic Regression, Decision Tree, SVM, and Naive Bayes for heart disease prediction.