You are in:Home/Publications/Azar AT, Kumar SS, Inbarani HH, Hassanien AE (2016) Pessimistic Multi-granulation Rough set based Classification for Heart Valve Disease Diagnosis. International Journal of Modelling, Identification and Control (IJMIC), 26(1): 42-51.

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Azar AT, Kumar SS, Inbarani HH, Hassanien AE (2016) Pessimistic Multi-granulation Rough set based Classification for Heart Valve Disease Diagnosis. International Journal of Modelling, Identification and Control (IJMIC), 26(1): 42-51.
Authors: Not Available
Year: 2016
Keywords: rough set theory; pessimistic multi-granulation rough sets; PMGRS; heart valve data; data classification; heart valves; heart disease diagnosis; feature selection; cardiovascular disease.
Journal: Int. J. of Modelling, Identification and Control
Volume: 26
Issue: 1
Pages: 42 - 51
Publisher: Inderscience Enterprises Ltd.
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
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.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus