NDLI: Data mining cardiovascular risk factors

Content Provider	IEEE Xplore Digital Library
Author	Kajabadi, A. Saraee, M.H. Asgari, S.
Copyright Year	2009
Description	Author affiliation: Department of Computer, University of Isfahan Technology Iran (Saraee, M.H.) \|\| Department of medical Science, University of Isfahan, Iran (Asgari, S.) \|\| Faculty of postgraduate Studies, Department of Computer, University of Najafabad Islamic Azad, Iran (Kajabadi, A.)
Abstract	Nowadays, Medical centers collect various data in different diseases. Investigating these data and obtaining useful results and patterns with respect to the diseases are the aims of using these data. Great amount of these data and confusions results from, are problems to reach considerable conclusions. In this paper data mining is used to overcome this problem and to gain useful relationships among risk factors in cardiovascular diseases. With respect to the spreading and the share that these diseases have in death they have great importance. The technique used in this work is classification with decision trees and the software is used, is CART. The target variable (class) is LDL (Low Density Lipoprotein) and 27 predictor variables are used for the classification. By applying data mining on data about 1800 people in city of Isfahan, Iran results show that the most important variables with regards to LDL are of the level of cholesterol, age, body mass index, APOB, triglyceride, (APOB/APOA) and smoking.
Starting Page	1
Ending Page	5
File Size	152810
Page Count	5
File Format	PDF
ISBN	9781424447398
DOI	10.1109/ICAICT.2009.5372552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-14
Publisher Place	Azerbaijan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Decision Tree Cardiac disease Data Mining Cardiovascular disease Data mining Cities and towns Cardiology Decision trees Cardiovascular diseases Medical diagnostic imaging Testing Classification tree analysis
Content Type	Text
Resource Type	Article

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