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Predication of Lung Nodule Characteristic Rating using Best Classifier Model

by Vinay. K, Ashok Rao, G. Hemantha Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 18
Year of Publication: 2012
Authors: Vinay. K, Ashok Rao, G. Hemantha Kumar
10.5120/9001-3142

Vinay. K, Ashok Rao, G. Hemantha Kumar . Predication of Lung Nodule Characteristic Rating using Best Classifier Model. International Journal of Computer Applications. 56, 18 ( October 2012), 29-32. DOI=10.5120/9001-3142

@article{ 10.5120/9001-3142,
author = { Vinay. K, Ashok Rao, G. Hemantha Kumar },
title = { Predication of Lung Nodule Characteristic Rating using Best Classifier Model },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 18 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number18/9001-3142/ },
doi = { 10.5120/9001-3142 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:12.149322+05:30
%A Vinay. K
%A Ashok Rao
%A G. Hemantha Kumar
%T Predication of Lung Nodule Characteristic Rating using Best Classifier Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 18
%P 29-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we are exploring the response of individual classifier families on imbalanced medical data. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how will be the response of different categories of classifier on imbalanced dataset. We are considering five categories of dataset which are grouped as, Instance Based classifier, Rule Based classifiers, Functional Classifier, Decision Tree classifier and Ensemble of Classifiers. The results from our experiments will be evaluated based on following performance metrics such as Accuracy, Precision, Recall, F-measure, Area under curve and kappa statistics.

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Index Terms

Computer Science
Information Sciences

Keywords

Ensemble of classifier Decision Tree Kappa Statistics

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