UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 7
July-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1807437


Registration ID:
185040

Page Number

593-599

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Title

Forecast of Heart Disease using K-means and SVM

Abstract

In medical sciences prediction of Heart disease is most difficult task. In India, main causes of Death are due to Heart Diseases. The deaths due to heart disease in many countries occur due to work overload, mental stress and many other problems. It is found as main reason in adults is due to heart disease. Thus, for detecting heart disease of a patient, there arises a need to develop a decision support system. Data mining classification techniques, namely Modified K-means and SVM are analyzed on Heart Disease is proposed in this Paper.

Key Words

Data Mining, Heart Disease, Machine Learning, Decision Support, Modified K-Means, SVM

Cite This Article

"Forecast of Heart Disease using K-means and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.593-599, July-2018, Available :http://www.jetir.org/papers/JETIR1807437.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Forecast of Heart Disease using K-means and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp593-599, July-2018, Available at : http://www.jetir.org/papers/JETIR1807437.pdf

Publication Details

Published Paper ID: JETIR1807437
Registration ID: 185040
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 593-599
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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