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Dimensionality Reduction Techniques for Improved Diagnosis of Heart Disease

by Swati Shilaskar, Ashok Ghatol
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 5
Year of Publication: 2013
Authors: Swati Shilaskar, Ashok Ghatol
10.5120/9921-4538

Swati Shilaskar, Ashok Ghatol . Dimensionality Reduction Techniques for Improved Diagnosis of Heart Disease. International Journal of Computer Applications. 61, 5 ( January 2013), 1-8. DOI=10.5120/9921-4538

@article{ 10.5120/9921-4538,
author = { Swati Shilaskar, Ashok Ghatol },
title = { Dimensionality Reduction Techniques for Improved Diagnosis of Heart Disease },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 5 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number5/9921-4538/ },
doi = { 10.5120/9921-4538 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:15.479305+05:30
%A Swati Shilaskar
%A Ashok Ghatol
%T Dimensionality Reduction Techniques for Improved Diagnosis of Heart Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 5
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical diagnosis is an important task that needs to be executed accurately and efficiently. Medical domain complexities are represented by multidimensional heterogeneous datasets. Computer aided diagnosis must deal with processing and analyzing high dimensional data. Optimization of features in datasets reduces time and memory complexity of learning algorithms. It is necessary to have a tool that gives relationship between features and eliminate redundant ones. Feature selection or feature extraction reduce dimensions and essentially influence the performance of classifier. Many techniques have been used to determine essential features of medical data. We investigate two feature extraction techniques, Principal component analysis (PCA) and common Factor Analysis (FA) techniques for classification of heart disease. These techniques expose the structure, while maintaining the integrity of the data, thus improving diagnosis performance.

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Index Terms

Computer Science
Information Sciences

Keywords

Dimensionality reduction PCA FA neural networks AUC heart disease

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