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Abstract
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Article Information:
A Methodology for Heart Disease Diagnosis Using Data Mining Technique
R. Kavitha and E. Kannan
Corresponding Author: R. Kavitha
Submitted: June 14, 2014
Accepted: July 19, 2014
Published: September 20, 2014 |
Abstract:
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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.
Key words: Data mining, heart disease diagnosis system, navie bayes classification, , , ,
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Cite this Reference:
R. Kavitha and E. Kannan, . A Methodology for Heart Disease Diagnosis Using Data Mining Technique. Research Journal of Applied Sciences, Engineering and Technology, (11): 1350-1354.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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