HEART DISEASE PREDICTION BASED ON PHYSIOLOGICAL PARAMETERS USING ENSEMBLE CLASSIFIER AND PARAMETER OPTIMIZATION

Abstract

This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.