Classification of HRS using SVM

Classification of HRS using SVM

Astha Ameta

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Kalpana Jain

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1 College of technology and engineering

2 College of Technology and Engineering/Maharana Pratap University of Agriculture and Technology

GJCST Volume 17 Issue C1

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CSTSDE09ZF2

The kidney diseases are one of the main causes of death around the world. Automatic detection and classification of kidney related diseases are important for diagnosis of kidney irregularities. Hepatorenal Syndrome (HRS) is a life-threatening medical condition when kidney fails due to liver failure. The treatment to such cases is liver transplant, or dialysis for temporary basis. This paper proposed to apply the Support Vector Machine (SVM) classification for diagnosis of HRS. The results were evaluated using realistic data from hospitals. RBF kernel function is used along with SVM. The results show a significant accuracy of 95%.

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References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Astha Ameta. 2017. \u201cClassification of HRS using SVM\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 17 (GJCST Volume 17 Issue C1): .

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GJCST Volume 17 Issue C1
Pg. 25- 30

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

H.5.5, D.2.5

Submission ReceivedDecember 11, 2016
RevisedDecember 11, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

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Issue date

April 26, 2017

Language

English

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Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Astha Ameta, Kalpana Jain (PhD/Dr. count: 0)

View Count (all-time): 297

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Publish Date: 2017 04, Wed

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Total Views: 6779

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