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Article:Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data

by Mrs.Shomona Gracia Jacob, Dr. R.Geetha Ramani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 7
Year of Publication: 2011
Authors: Mrs.Shomona Gracia Jacob, Dr. R.Geetha Ramani
10.5120/3920-5521

Mrs.Shomona Gracia Jacob, Dr. R.Geetha Ramani . Article:Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data. International Journal of Computer Applications. 32, 7 ( October 2011), 46-53. DOI=10.5120/3920-5521

@article{ 10.5120/3920-5521,
author = { Mrs.Shomona Gracia Jacob, Dr. R.Geetha Ramani },
title = { Article:Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 7 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 46-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number7/3920-5521/ },
doi = { 10.5120/3920-5521 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:36.369594+05:30
%A Mrs.Shomona Gracia Jacob
%A Dr. R.Geetha Ramani
%T Article:Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 7
%P 46-53
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper highlights the significance of classification in data mining and knowledge discovery. In this paper we investigate the performance of various data mining classification algorithms viz. Rnd Tree, Quinlan decision tree algorithm (C4.5), K-Nearest Neighbor algorithm etc., on a large dataset from the ‘Wisconsin Breast tissue dataset’ (derived from the UCI Machine Learning Repository) that comprises of 11 attributes and 106 instances. The results of this study indicate the level of accuracy and other performance measures of the algorithms in detecting the presence of breast cancer and the associated breast tissue conditions that increase the risk of developing cancer in future. Moreover the importance of feature selection/reduction in improving the performance of classification algorithms is also described. The classification algorithm Rnd Tree produced 100 percent accuracy for classification of all the training data under multiple classes. The classification algorithm was also applied to verify it’s correctness in classifying test data.

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Index Terms

Computer Science
Information Sciences

Keywords

Knowledge Patterns Pattern Recognition Clinical Data Healthcare Breast Cancer Breast Tissue Classification