Title: Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis
Authors: Ahmad Taher Azar; S. Senthil Kumar; H. Hannah Inbarani; Aboul Ella Hassanien
Addresses: Faculty of Computers and Information, Benha University, Benha, Egypt ' Department of Computer Science, Periyar University, Salem, Tamilnadu, 636011, India ' Department of Computer Science, Periyar University, Salem, Tamilnadu, 636011, India ' Faculty of Computer and Information, Cairo University, Cairo, Egypt
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.
Keywords: rough set theory; pessimistic multi-granulation rough sets; PMGRS; heart valve data; data classification; heart valves; heart disease diagnosis; feature selection; cardiovascular disease.
International Journal of Modelling, Identification and Control, 2016 Vol.26 No.1, pp.42 - 51
Available online: 14 Jul 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article