Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

Abstract

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease.

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

“…However, data used for this study 49 was very limited (only 303 records were used in this study). Following, a study conducted by Asif et al 50 precision and specificity were reported as 95.08 and 98.09, respectively. However, the analysis 50 was tested on very limited data (only 1625 records were used in this study).…”

“…While a model may perform well in balanced circumstances, it might struggle in imbalanced situations in real life 26,27 . To truly assess the effectiveness of proposed approaches 48,49,50 , it is crucial to test them in unbalanced scenarios using a larger number of samples. The performance measurements across various unbalanced circumstances should be thoroughly described in order to appropriately assess the success of the suggested approaches in real-life situations.…”

“…However, the proposed study was conducted using 303 records. To predict cardiac disease, the study by Asif et al 50 proposed an ensemble learning (extra tree classifier, random forest, XGBoost, and CatBoost) model that integrates multiple preprocessing stages, hyperparameter optimization approaches, and ensemble learning algorithms. Multiple datasets from Kaggle have been merged to conduct this analysis.…”

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

“…Through bootstrapping, distinct subsets of the training data are created by random sampling with replacement from the original dataset. This helps in reducing overfitting by providing a more robust and generalizable model [42,43]. At each node of the decision tree, a random subset of features is considered for each split instead of using all features for each split.…”

“…This helps in decorrelating the trees and introduces diversity in the ensemble. The RF algorithm involves the tuning of multiple hyperparameters, including the n estimators (count of decision trees for growth in the forest), max depth of each decision tree in the forest, criterion (a function used to evaluate split quality at each node of the trees, for example, Gini impurity and information entropy) [42]. More trees in the forest generally results in more reliable and accurate predictions [43].…”