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Mining Health Data using Weighted Approach

by P. Priyanga, Naveen N. C.
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 10
Year of Publication: 2016
Authors: P. Priyanga, Naveen N. C.
10.5120/cae2016652381

P. Priyanga, Naveen N. C. . Mining Health Data using Weighted Approach. Communications on Applied Electronics. 5, 10 ( Sep 2016), 1-6. DOI=10.5120/cae2016652381

@article{ 10.5120/cae2016652381,
author = { P. Priyanga, Naveen N. C. },
title = { Mining Health Data using Weighted Approach },
journal = { Communications on Applied Electronics },
issue_date = { Sep 2016 },
volume = { 5 },
number = { 10 },
month = { Sep },
year = { 2016 },
issn = { 2394-4714 },
pages = { 1-6 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume5/number10/655-2016652381/ },
doi = { 10.5120/cae2016652381 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:55:10.619418+05:30
%A P. Priyanga
%A Naveen N. C.
%T Mining Health Data using Weighted Approach
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 10
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web Analytics (WA) is a vital area in the field of Data Mining (DM) that works with the principle of extracting interesting information or knowledge from the World Wide Web. WA is the measurement, collection, analysis and reporting of Internet data. The research in WA has led to the development of new techniques to generate automated topic hierarchies and web dictionaries. WA plays a major role in health care domain, to search health related information required from the web. Gathering knowledge about health has become a complex procedure for the majority of users. This confuses the users and consuming more time in overloaded data that continue to enlarge. Applications of DM to Web-page ranking helps Web search engines to find high quality web pages. In this paper Machine Learning (ML) methods for extracting knowledge from the large medical data on the Internet which is heterogeneous in nature of the web is proposed. The main objective is to develop a fast and efficient algorithm for real-time processing of big data and create knowledge out of the existing information in the web.

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Index Terms

Computer Science
Information Sciences

Keywords

Web Analytics; Health care; Big Data; Machine Learning

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