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:
JETIR1807866


Registration ID:
185959

Page Number

922-928

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Title

Data Analytics on Chronic Kidney Disease Data Set Using Association Rule Mining

Abstract

Data analytics is the science of drawing insights from unrefined data/information from various sources. In order to increase the overall efficiency of a business or system, data analytic techniques help to make trends and metrics which will be useful to optimize the processes. Scientists and researchers to verify or disprove scientific models, theories and hypotheses, Data analytics technologies and techniques are widely used especially in commercial & healthcare industries to enable organizations to make knowledge based decisions efficiently. Among many industries, health care is getting prominence as they serve people directly with health and policy issues. In this paper, Chronic Kidney Disease (CKD) data is considered to predict the factors which contribute to the diseases related to kidney commonly called as chronic kidney disease and this prediction can be done with data mining techniques. CKD is one of the fast growing diseases which can cause kidney failure or can lead to death. Research is going on this disease to predict the causes of CKD and symptoms of CKD. Association rule mining technique is applied on chronic kidney disease using well known algorithm apriori using WEKA tool & R to predict the rules. Based on the interest of experts, interested association rules are identified which will be helpful to medical practitioners, hospital management, experts, health analysts, pharmacists, pharmaceutical companies, policy makers, insurance companies and others. The interested rules are useful to predict CKD and also used to take precautions to prevent CKD.CKD can be prevented by reducing or controlling levels of some factors like diabetes, hypertension, sodium, potassium levels etc. Also, this paper computes execution time and compares algorithm with tools. The main objective of this research is to reduce the CKD cases by proposing different conditions which can lead to CKD and also gives different conditions to prevent from CKD.

Key Words

Data Analytics, Association Rule Mining, Health Care Industry, Chronic Kidney Disease

Cite This Article

"Data Analytics on Chronic Kidney Disease Data Set Using Association Rule Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.922-928, July-2018, Available :http://www.jetir.org/papers/JETIR1807866.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

"Data Analytics on Chronic Kidney Disease Data Set Using Association Rule Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp922-928, July-2018, Available at : http://www.jetir.org/papers/JETIR1807866.pdf

Publication Details

Published Paper ID: JETIR1807866
Registration ID: 185959
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 922-928
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162


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