Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

• Published in: Algorithms Vol. 16; no. 6; p. 308
• Main Authors: Asif, Daniyal, Bibi, Mairaj, Arif, Muhammad Shoaib, Mukheimer, Aiman
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Accuracy; Algorithms; Cardiovascular disease; Data mining; Data search; Datasets; Decision trees; Disease prevention; Ensemble learning; extra tree; Health aspects; Heart; heart disease; Heart diseases; hyperparameter optimization; Machine learning; Mathematical optimization; Medical research; Medicine, Experimental; Methods; Mortality; Neural networks; Optimization; Optimization techniques; Predictions; Prognosis; Public health; Regression analysis; Support vector machines; Taiwan; XGBoost; Taiwan
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16060308

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.