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Published
Nov 30, 2014
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Abstract

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining techniques are used for variety of applications. Data mining techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in clinical data. One of the most important applications of such systems is in diagnosis of heart disease. The main objective of Enhanced Heart Disease Analysis and Prediction System (EHDAPS) is predicting the heart disease using historical heart database. To develop this system, medical terms such as sex, blood pressure, and cholesterol like seventeen input attributes are used. In this paper association among various attributes which are the causative factors of heart diseases are analyzed. The patient’s records are observed before prediction and the factors are grouped as per its severity level.  In this system the level of causative factors are categorized using K-Means clustering technique and it distinguishes the risky and non risky factors.  Frequent risk factors are mined from the clinical heart database using Apriori algorithm. The risk factors are taken for this study to predict the risk level and find the co-ordination among the factors that helps the medical people to predict the disease with minimum tests and treatments.

Keywords: Heart disease, Data mining, K-Means Clustering, Apriori algorithm

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Author Biographies

Alagugowri. S, Government Arts College, Udumalpet – 642 126

Research Scholar, Department of Computer Science

Dr. T. Christopher, Government Arts College Udumalpet - 642 126

Assistant Professor& HOD, Department of Computer Science
How to Cite
S, A., & Christopher, D. T. (2014). Enhanced Heart Disease Analysis and Prediction System [EHDAPS] Using Data Mining. International Journal of Emerging Trends in Science and Technology, 1(09). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/422

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