# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 3 Proposed Model
### Classifier Selection  
The proposed heart disease prediction model leverages ensemble learning, combining multiple base classifiers to improve predictive accuracy and robustness. In our study, we select a diverse set of base classifiers, including:  
* Support Vector Machine (SVM): SVM is an effective ML technique that is commonly employed for problems involving classification and regression [36; 37]. The algorithm operates by identifying the hyperplane that achieves the highest degree of separation between distinct classes of data, while simultaneously increasing the margin between these classes. SVM exhibits notable efficacy in high-dimensional areas and possesses the ability to handle both linear and non-linear associations among data points. The technology in question is adaptable and extensively employed across diverse disciplines such as image recognition, text classification, and bioinformatics. Consequently, it holds significant value as an essential asset for data scientists and practitioners of ML. * Decision Tree: The Decision Tree method is a fundamental ML technique that facilitates decision-making processes by organizing data into a hierarchical structure that resembles a tree [38]. The method is extensively employed for tasks involving classification and regression, offering a clear and comprehensible approach to gen  
Figure 4: Feature importance applying extra tree classifier. erating predictions based on input data. Decision trees are frequently employed in sophisticated ensemble techniques, rendering them a valuable asset in the field of ML. * K-Nearest Neighbor (KNN): KNN is a versatile classification and regression technique [39]. The 'k' nearest data points in the training set are used to produce predictions in KNN. In regression, the average neighbor value predicts the query point's class label, while in classification, the majority class determines it. KNN works in many applications, since it does not make any assumptions about data distributions. * Logistic Regression: Logistic Regression is a widely employed statistical and ML model utilized for binary classification assignments [40; 41]. Contrary to its nomenclature, this method is not employed for regression purposes; rather, it is utilized to estimate the likelihood of an input being classified into one of two distinct categories. This process involves the utilization of a sigmoid function to model the data, resulting in a mapping of input properties to a probability score ranging from 0 to 1. Logistic Regression is characterized by its simplicity, interpretability, and computational efficiency, rendering it a valuable analytical technique across many fields such as medical diagnosis, spam detection, and credit scoring. The linear model possesses the capability to be expanded to accommodate multi-class categorization, rendering it highly adaptable in practical applications. * Adaboost: AdaBoost, also known as Adaptive Boosting, is a ML ensemble method employed to enhance the efficacy of weak classifiers [42]. The algorithm operates by iteratively training a sequence of weak classifiers, assigning higher importance to the cases that were incorrectly identified by the preceding classifiers. This approach directs the succeeding classifiers towards the more difficult cases, resulting in a robust and precise model. AdaBoost's adaptability allows it to handle various types of healthcare data, including clinical, genetic, or imaging data. Its feature selection capability can help prioritize important factors in disease diagnosis, and its reduced risk of overfitting ensures robust performance even in the presence of noisy or incomplete data. Overall, AdaBoost is a valuable tool for improving the accuracy and reliability of disease prediction models, which can have a significant impact on early diagnosis and treatment planning. * Naive Bayes: The Naive Bayes algorithm is a widely utilized probabilistic classification technique that is known for its simplicity and effectiveness in the fields of ML and natural language processing [43].