An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction Online publication date: Tue, 22-Nov-2022
by Sibo Prasad Patro; Neelamadhab Padhy; Rahul Deo Sah
International Journal of Modelling, Identification and Control (IJMIC), Vol. 41, No. 1/2, 2022
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook