An Ensemble Machine Learning Strategy for Accurate and Timely Prediction of Heart Disease
DOI:
https://doi.org/10.59796/jcst.V16N1.2026.166Keywords:
heart disease, classification, machine learning, ensemble, bagging, boosting, stackingAbstract
Heart disease is a complicated disorder that is becoming increasingly common. Finding the best treatment plan for any heart disease patient requires early detection. The main goal of this study is to improve the prediction of heart disease by implementing ensemble learning techniques with machine learning (ML) models. The dataset used in this study, comprising 319,755 records from the Centers for Disease Control and Prevention, was downloaded from the Kaggle website. The SMOTE-ENN hybrid approach was used to address class imbalance. To increase the consistency and distribution of numerical variables, data preprocessing entailed standardization and the creation of dummy variables. The machine learning (ML) models Random Forest, Logistic Regression, Support Vector Machine, and Extra Trees were applied to the processed dataset without and with bagging, boosting, and stacking ensemble methods. Stacking, which combined SVM and Logistic Regression, outperformed the baseline models and other ensemble techniques, returning the highest recall score of 0.837731. This study underlines the significance of data balancing and ensemble learning for accurate forecasts based on medical datasets, which are typically large. The findings highlight how ML can enhance early diagnosis and intervention in the treatment of cardiac disease.
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