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Reseach Article

An Empirical Comparison of Data Mining Techniques in Medical Databases

by Kittipol Wisaeng
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 7
Year of Publication: 2013
Authors: Kittipol Wisaeng
10.5120/13408-1061

Kittipol Wisaeng . An Empirical Comparison of Data Mining Techniques in Medical Databases. International Journal of Computer Applications. 77, 7 ( September 2013), 23-27. DOI=10.5120/13408-1061

@article{ 10.5120/13408-1061,
author = { Kittipol Wisaeng },
title = { An Empirical Comparison of Data Mining Techniques in Medical Databases },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 7 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number7/13408-1061/ },
doi = { 10.5120/13408-1061 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:23.377325+05:30
%A Kittipol Wisaeng
%T An Empirical Comparison of Data Mining Techniques in Medical Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 7
%P 23-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. This paper present the basic data mining techniques i. e. , naive Bayesian tree, RIpple DOwn Rule, naive Bayes and decision tree algorithm J48 for classifying in medical databases. The goal of this paper is to provide a comprehensive of different classifying techniques in data mining. To evaluate the performance of the above techniques recall, precision and accuracy measures are applied.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining naïve Bayesian tree RIpple DOwn Rule naïve Bayes J48