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Data Mining in Atherosclerosis Risk Factor Data
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Author(s): Petr Berka (University of Economics and the Academy of Sciences of the Czech Republic, Czech Republic), Jan Rauch (University of Economics and the Academy of Sciences of the Czech Republic, Czech Republic)and Marie Tomecková (Academy of Sciences, Czech Republic)
Copyright: 2009
Pages: 22
Source title:
Data Mining and Medical Knowledge Management: Cases and Applications
Source Author(s)/Editor(s): Petr Berka (University of Economics, Prague, Czech Republic), Jan Rauch (University of Economics, Prague, Czech Republic)and Djamel Abdelkader Zighed (University of Lumiere Lyon 2, France)
DOI: 10.4018/978-1-60566-218-3.ch018
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Abstract
The aim of this chapter is to describe goals, current results, and further plans of long-time activity concerning application of data mining and machine learning methods to the complex medical data set. The analyzed data set concerns a longitudinal study of atherosclerosis risk factors. The structure and main features of this data set, as well as methodology of observation of risk factors, are introduced. The important first steps of analysis of atherosclerosis data are described in details together with a large set of analytical questions defined on the basis of first results. Experience in solving these tasks is summarized and further directions of analysis are outlined.
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