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A new approach of rough set theory for feature selection and bayes net classifier applied on heart disease dataset
Joint Authors
al-Shammari, Iman Salih
al-Ubaydi, Ali Abd Rahumi
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 26, Issue 2 (28 Feb. 2018), pp.15-26, 12 p.
Publisher
Publication Date
2018-02-28
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
-In this paper a new approach of rough set features selection has been proposed.
Feature selection has been used for several reasons a) decrease time of prediction b) feature possibly is not found c) present of feature case bad prediction.
Rough set has been used to select most significant features.
The proposed rough set has been applied on heart diseases data sets.
The main problem is how to predict patient has heart disease or not depend on given features.
The problem is challenge, because it cannot determine decision directly .Rough set has been modified to get attributes for prediction by ignored unnecessary and bad features.
Bayes net has been used for classified method.
10-fold cross validation is used for evaluation.
The Correct Classified Instances were 82.17, 83.49, and 74.58 when use full, 12, 7 length of attributes respectively.
Traditional rough set has been applied, the minimum Correct Classified Instances were 58.41 and 81.51 when use 2 length of attributes respectively.
American Psychological Association (APA)
al-Shammari, Iman Salih& al-Ubaydi, Ali Abd Rahumi. 2018. A new approach of rough set theory for feature selection and bayes net classifier applied on heart disease dataset. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 26, no. 2, pp.15-26.
https://search.emarefa.net/detail/BIM-1094074
Modern Language Association (MLA)
al-Shammari, Iman Salih& al-Ubaydi, Ali Abd Rahumi. A new approach of rough set theory for feature selection and bayes net classifier applied on heart disease dataset. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 26, no. 2 (2018), pp.15-26.
https://search.emarefa.net/detail/BIM-1094074
American Medical Association (AMA)
al-Shammari, Iman Salih& al-Ubaydi, Ali Abd Rahumi. A new approach of rough set theory for feature selection and bayes net classifier applied on heart disease dataset. Journal of Babylon University : Journal of Applied and Pure Sciences. 2018. Vol. 26, no. 2, pp.15-26.
https://search.emarefa.net/detail/BIM-1094074
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 25-26
Record ID
BIM-1094074