Cardiovascular Disease Prediction through Machine Learning: A Comparative Study of Ensemble Techniques
DOI:
https://doi.org/10.1807/2f7s6c18Keywords:
Heart disease prediction, Machine Learning, heart dataset, gradient boosting, Adaboost , Random Forest, XGBoost, CatBoost, Light GBM, Extra tree classifier, Ensemble Learning, Bagging, BoostingAbstract
Cardiovascular disease is a worldwide health issue that necessitates enhancements in the evaluation of risks and early identification methods. Several lethal heart illnesses have been transmitted to humans through various recognized mechanisms. According to a figure from the World Health Organization (WHO), over 17.9 million individuals in the country perish annually, representing 32% of global mortality. The increasing annual population poses a significant challenge in terms of early-stage diagnosis and treatment. Machine learning approaches have proven highly useful in the realm of healthcare. The study employed the "heart dataset" obtained from Kaggle. The implementation was carried out using the Open editor and the Python programming language. This work aims to calculate and assess the precision of ensemble machine learning algorithms in predicting cardiac disease, along with other performance metrics. The study examined seven proven models, including Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM (boosting techniques), and Random Forest and Extra Tree Classifier (bagging algorithm). These models were tested and trained using the heart dataset. 80% of the dataset will be allocated for training, while the remaining 20% will be used for testing. This study examines machine learning methods to determine the optimal model for predicting cardiac disease. Upon assessing multiple models, we determined that XGBoost achieves the highest accuracy and F1-score, with values of 87.50% and 88.44% respectively, surpassing the performance of other models we employed.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
