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Reseach Article

An Efficient Medical Data Classification based on Ant Colony Optimization

by Jyotsna Bansal, Divakar Singh, Anju Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 10
Year of Publication: 2014
Authors: Jyotsna Bansal, Divakar Singh, Anju Singh
10.5120/15243-3785

Jyotsna Bansal, Divakar Singh, Anju Singh . An Efficient Medical Data Classification based on Ant Colony Optimization. International Journal of Computer Applications. 87, 10 ( February 2014), 14-19. DOI=10.5120/15243-3785

@article{ 10.5120/15243-3785,
author = { Jyotsna Bansal, Divakar Singh, Anju Singh },
title = { An Efficient Medical Data Classification based on Ant Colony Optimization },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 10 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number10/15243-3785/ },
doi = { 10.5120/15243-3785 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:33.420934+05:30
%A Jyotsna Bansal
%A Divakar Singh
%A Anju Singh
%T An Efficient Medical Data Classification based on Ant Colony Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 10
%P 14-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the case of different diseases classification is an important aspect so that one can find the infected set efficiently. In this paper three different dataset named Leukemia, Lung Cancer and Prostate from the UCI machine learning repository are considered and apply efficient association based ant colony optimization for improving the classification accuracy. In our approach one can select the dataset. The data set has been refined according to the attributes. Then final data set is achieved on which we apply the next inabilities. The maximum threshold will be determined by finding the support value. So the support values are fetched and according to the support value, it will be categorized in two different parts that is relevant or irrelevant. In our case it is 0. 5. If the set crosses the maximum threshold then it will be qualify for the final set otherwise it is discarded. Then ACO mechanism has been applied on the final dataset to find the classification accuracy. Our results show the effectiveness of our approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Clustering Feature selection ACO