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.

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The authors have no competing interests.