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Analysis of Attribute Association Rule from Large Medical Datasets towards Heart Disease Prediction

Author(s): D. P. Shukla | Shamsher Bahadur Patel | Ashish Kumar Sen | Pramod Kumar Yadav

Journal: International Journal of Computer Science and Mobile Computing
ISSN 2320-088X

Volume: 2;
Issue: 8;
Start page: 91;
Date: 2013;
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Keywords: Data mining | Association Rule Mining | Decision Tree | Machine Learning Technique

Cardio vascular disease is a major threat to half of the world population. The term heart diseaseis related to all the diverse diseases affecting the heart. The healthcare industry generates large amount ofdata that are too difficult to be analyzed by traditional methods. Hence computer assisted methods arenecessary to make correct decisions. Heart disease is a term that assigns to a large number of medicalconditions related to heart. These medical conditions describe the abnormal health conditions that directlyinfluence the heart and all its parts. The main issue about mining association rules in a medical data is thelarge number of rules that are discovered, most of which are irrelevant. A rule-based decision support system(DSS) is presented for the diagnosis of coronary vascular disease (CVD). Such number of rules makes thesearch slow. However, not all of the generated rules are interesting, and some rules may be ignored. Inmedical terms, association rules relate disease data measures the patient risk factors and occurrence of thedisease. Association rules are compared to predictive rules mined with decision trees, a well-known machinelearning technique. In this paper we propose a new system to find the strength of association among theattributes of a given data set. The proposed system has several advantages since it is automatically generated.It provides CVD diagnosis based on easily and none invasively acquired features.

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