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Data Mining Techniques for Diagnostic Support of Glaucoma using Stratus OCT and Perimetric Data

by Kinjan Chauhan, Prashant Chauhan, Anand Sudhalkar, Kalpesh Lad, Ravi Gulati
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 8
Year of Publication: 2016
Authors: Kinjan Chauhan, Prashant Chauhan, Anand Sudhalkar, Kalpesh Lad, Ravi Gulati
10.5120/ijca2016911855

Kinjan Chauhan, Prashant Chauhan, Anand Sudhalkar, Kalpesh Lad, Ravi Gulati . Data Mining Techniques for Diagnostic Support of Glaucoma using Stratus OCT and Perimetric Data. International Journal of Computer Applications. 151, 8 ( Oct 2016), 34-39. DOI=10.5120/ijca2016911855

@article{ 10.5120/ijca2016911855,
author = { Kinjan Chauhan, Prashant Chauhan, Anand Sudhalkar, Kalpesh Lad, Ravi Gulati },
title = { Data Mining Techniques for Diagnostic Support of Glaucoma using Stratus OCT and Perimetric Data },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 8 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number8/26256-2016911855/ },
doi = { 10.5120/ijca2016911855 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:35.596692+05:30
%A Kinjan Chauhan
%A Prashant Chauhan
%A Anand Sudhalkar
%A Kalpesh Lad
%A Ravi Gulati
%T Data Mining Techniques for Diagnostic Support of Glaucoma using Stratus OCT and Perimetric Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 8
%P 34-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

People suffer from so many diseases in today’s era. It has become imperative to find either solutions to these diseases or detect them during early stages so that they can be prevented or cured. Glaucoma is one of the eye disorders and is one of the leading causes of blindness. It is a disease that gradually degenerates the eye vessels causing vision loss in the patient. This paper discusses the data mining techniques like Decision Tree, Linear Regression and Support Vector Machine that have been used for diagnosis of glaucoma in the retinal image. Parameters obtained from Perimetry and Stratus Optic Coherence Test (OCT) have been fed to each technique to find out their performance in terms of accuracy, sensitivity and specificity. The researcher have compared results obtained from the Decision Tree, Linear Regression and Support Vector Machine (SVM) and found that Decision Tree and Linear Regression Model performs much better than SVM for diagnosis of Glaucoma giving accuracy of 99.17%, 92.56% and 70.25% respectively. The specificity of Linear Regression and SVM is 97.56% and 96.34% respectively.

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Index Terms

Computer Science
Information Sciences

Keywords

Glaucoma Cup to Disc Ratio(CDR) Data Mining Decision Tree SVM and Linear Regression.

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