Title: An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction
Authors: Sibo Prasad Patro; Neelamadhab Padhy; Rahul Deo Sah
Addresses: School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur, India ' School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur, India ' Department of Computer Application and Information Technology, Dr. Shyama Prasad Mukherjee University, Ranchi, India
Abstract: Coronary heart disease (CHD) is one of the most common heart disease types in the world. It becomes a frequent cause of mortality due to a lack of proper medical diagnosis, technology, and a healthy lifestyle. The machine learns patterns from an existing dataset and applies different rules to predict the outcome. Classification is a powerful machine learning technique for prediction. In this work, we propose a new ensemble classification model by combining multiple classifiers for improving the accuracy of weak algorithms. An ensemble classifier was applied by using a majority vote-based technique for cardiovascular disease prediction and classification. A three-dimensionality approach is applied to Cleveland dataset from the UCI repository. The average accuracy of each method is calculated as PCA (0.8636), K-PCA (0.8630), and LDA (0.90). Compared to PCA and K-PCA, higher accuracy is achieved by LDA. LDA is used as the best dimensionality reduction technique.
Keywords: ensemble methods; voting classifier; coronary heart disease; CHD; bagging classifier; stacking classifier; AdaBoost classifier.
International Journal of Modelling, Identification and Control, 2022 Vol.41 No.1/2, pp.68 - 86
Received: 03 Jul 2021
Accepted: 06 Oct 2021
Published online: 22 Nov 2022 *