UGC Approved Journal no 63975

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:
JETIRA006043


Registration ID:
185449

Page Number

249-254

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Title

SUBSTANTIATION STUDY FOR DISEASE PREDICTION

Abstract

Statistics in Data Mining is used to identify frequent item sets and its correlation. The broad application of association of Statistics and Data mining in research, market analysis and disease predictions are proved. The identification of influencing factors of the disease is very peculiar work in medical diagnosis. Early detection is always better to reduce disease complexity, time and money expenses also. In this paper we are implementing Statistical Data mining techniques to predict disease.

Key Words

Data mining; Naïve bayes classifier; bayesian belief network; FOIL Algorithm; rule- pruning; rule generation; C4.5 decision tree

Cite This Article

"SUBSTANTIATION STUDY FOR DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.249-254, July-2018, Available :http://www.jetir.org/papers/JETIRA006043.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

"SUBSTANTIATION STUDY FOR DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp249-254, July-2018, Available at : http://www.jetir.org/papers/JETIRA006043.pdf

Publication Details

Published Paper ID: JETIRA006043
Registration ID: 185449
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 249-254
Country: -, -, -- .
Area: Engineering
ISSN Number: 2349-5162


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