You are in:Home/Publications/Kumar SS, Inbarani HH, Azar AT, Polat K (2017) Covering-based rough set classification system. Neural Computing and Applications, 28(10): 2879–2888, Springer. DOI 10.1007/s00521-016-2412-7 [ISI Indexed: Impact Factor: 4.215].

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

Title:
Kumar SS, Inbarani HH, Azar AT, Polat K (2017) Covering-based rough set classification system. Neural Computing and Applications, 28(10): 2879–2888, Springer. DOI 10.1007/s00521-016-2412-7 [ISI Indexed: Impact Factor: 4.215].
Authors: Not Available
Year: 2017
Keywords: Rough set; Covering-based rough set (CRS)
Journal: Neural Computing and Applications
Volume: 28
Issue: 10
Pages: 2879–2888
Publisher: Springer
Local/International: International
Paper Link:
Full paper Ahmad Taher Azar_first_page.pdf
Supplementary materials Not Available
Abstract:

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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