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The Survey Of The Heart Disease Detection Technques In Data Mining

File Size:
1.38 MB
Volume:
Volume 3, Issue 7 (July, 2017)
Publication No:
IJTC201707002
Author:
Vinny Sareen, Er. Sukhpreet Kaur
Downloads:
1 x

Abstract:
Data mining is the process of collecting large sets of data and then separating the meaning of data. Data mining provides the various techniques and methods for the transformation of data into useful information used for decision making in future. The data mining techniques, namely Decision Tree, Naïve Byes, Neural Network, K-means clustering, association classification, Support vector machine(SVM) and MAFIA algorithm are analyzed on heart disease database. Heart disease defines a various healthcare conditions that are highly vast in nature which are related to heart and has many basic causes that affect the entire body. In this paper, we survey different papers in which one or more algorithms of data mining are used for prediction of heart disease. This paper discussed the overall review of heart disease prediction using various classification techniques for heart disease. These techniques used in heart disease take less time and make process fast for the prediction system to predict heart disease with good accuracy in order to improve health.

Keywords:
Data mining, Heart Disease, Decision tree, K- means clustering, association classification, SVM.