Prediction of Heart Disease using Ensemble Learning

Objectives: To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is increase the prediction accuracy in model. Methods: proposed system experimented with by using Cleveland datasets collected from UCI repository. dataset consists 14 attributes. In we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random Forest and SVM along classifier. entire trained upon Pearson correlation coefficient selected features under k-fold cross-validation setup. Final outcome obtained aggregating accuracy. Findings: performance was validated compared Machine models. attains higher classification 95.33% than all other methods. Novelty: A novel has been better predicting disease. Keywords: Ensemble Model; Learning; Prediction; Accuracy; Kfold cross validation