A knowledge discovery from incomplete coronary artery disease datasets using rough set

Ahmad Fadzil, Mohd Hani (2011) A knowledge discovery from incomplete coronary artery disease datasets using rough set. International Journal of Medical Engineering and Informatics, Volume 3, Issue 1, March 2011, Pages 60-77 (1). pp. 60-77. ISSN 17550653

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Incompleteness of datasets is one of the important issues in the area of knowledge discovery in medicine. This study proposes a rough set theory (RST)-based knowledge discovery from coronary artery disease (CAD) datasets when there are only small number of objects and contain missing data (incomplete). At first, RST combined with artificial neural network (ANN) is developed to impute the missing data of the datasets. Then, the knowledge that is discovered from imputed datasets is used to evaluate the quality of the imputation. After that, RST is applied to extract rules from the imputed datasets. This will result in a large number of rules. Rule selection based on the quality of extracted rules is investigated. All the evaluation and selection are based on the complete datasets. Finally, the selected small number of rules is evaluated. The discovered selected rules are used as a classifier on the diagnosis of the presence of CAD to demonstrate their good performance.

Item Type: Article
Subjects: R Medicine > RB Pathology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE: Centre of Excellence > Center for Intelligent Signal and Imaging Research
Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
Depositing User: Prof Ir Dr Ahmad Fadzil Mohd Hani
Date Deposited: 21 Nov 2011 06:29
Last Modified: 31 Mar 2014 17:36
URI: http://scholars.utp.edu.my/id/eprint/6693

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