The full text of this article
Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis
by Ahmad Taher Azar; S. Senthil Kumar; H. Hannah Inbarani; Aboul Ella Hassanien
International Journal of Modelling, Identification and Control (IJMIC), Vol. 26, No. 1, 2016
Abstract: The primary contribution of this study relies on proposing a new method, which can detect heart diseases in respective heart valve data. In this work, supervised quick reduct feature selection algorithm is applied for selecting important features from heart valve data. The classification method is applied only for relevant features selected using supervised quick reduct from heart valve data. In this paper, a new classification approach based on pessimistic multi-granulation rough sets (PMGRS) is applied for heart valve disease diagnosis. In multi-granulation rough sets, set approximations are well-defined by multiple equivalence relations on the universe, leading to an effective model for classification. This is confirmed by experimental evaluation, which shows excellent classification performance and also demonstrates that the proposed approach is superior to other benchmark classification algorithms including naïve Bayes, multi-layer perceptron (MLP), and J48 and decision table classifiers.
Online publication date: Thu, 14-Jul-2016
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
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 email@example.com