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Reseach Article

Concept Adapting Real-Time Data Stream Mining for Health Care Applications

Published on March 2012 by Dipti D. Patil, Jyoti G. Mudkanna, Dnyaneshwar Rokade, Vijay M. Wadhai
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 9
March 2012
Authors: Dipti D. Patil, Jyoti G. Mudkanna, Dnyaneshwar Rokade, Vijay M. Wadhai
a72defd2-6f9c-4677-a7b7-bb16876cfb94

Dipti D. Patil, Jyoti G. Mudkanna, Dnyaneshwar Rokade, Vijay M. Wadhai . Concept Adapting Real-Time Data Stream Mining for Health Care Applications. International Conference in Computational Intelligence. ICCIA, 9 (March 2012), 30-35.

@article{
author = { Dipti D. Patil, Jyoti G. Mudkanna, Dnyaneshwar Rokade, Vijay M. Wadhai },
title = { Concept Adapting Real-Time Data Stream Mining for Health Care Applications },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 9 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 30-35 },
numpages = 6,
url = { /proceedings/iccia/number9/5153-1065/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Dipti D. Patil
%A Jyoti G. Mudkanna
%A Dnyaneshwar Rokade
%A Vijay M. Wadhai
%T Concept Adapting Real-Time Data Stream Mining for Health Care Applications
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 9
%P 30-35
%D 2012
%I International Journal of Computer Applications
Abstract

Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Vital signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria is to solve the realtime data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in health care environment

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Index Terms

Computer Science
Information Sciences

Keywords

Real-time data stream mining concept-drift vital Signal processing Health Care