Research Article | OPEN ACCESS
A Methodology for Heart Disease Diagnosis Using Data Mining Technique
R. Kavitha and E. Kannan
Department of CSE, Vel Tech Rangarajan Sagunthala R and D Institute of Science and Technology
(Vel Tech RR and SR Technical University), Chennai-62, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2014 11:1350-1354
Received: June ‎14, ‎2014 | Accepted: July ‎19, ‎2014 | Published: September 20, 2014
Abstract
Heart Disease diagnosis is done typically by doctor’s knowledge and training. But even then patients are requested to take more number of medical tests for diagnosis, in which all the tests does not contribute towards effective diagnosis of heart disease. There are nearly 15 attributes which are involved in the heart disease diagnosis process. The objective of this study is to identify the key patterns and feature subsets from the heart disease data set using the Naive Bayes classifier model. The proposed system identifies feature subsets of critical data instances in data sets. It identifies and removes the redundant attribute and inter correlated attribute. The 15 is reduced to 5 attribute using our diagnosis approach by which we can naturally reduce the computational time and cost of the process. In our proposed work we also find the critical nugget. Critical Nugget is a small collection of records or instances that contain domain-specific important information. It helps to reduce the irrelevant attribute and to find the top critical nuggets. The experimental results have validated to reduce the attribute and significantly improve the accuracy of the classification task.
Keywords:
Data mining, heart disease diagnosis system , navie bayes classification,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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