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Early diagnosis of Ischemia Stroke using neural network

Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.

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Bibliographic Details
Main Authors: Anita, Thakur, Surekha, Bhanot, Mishra, S.N.
Other Authors: anni.thakur@gmail.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis 2009
Subjects:
Feed forward neural network
MATLAB
Cerebral ischemia stroke
Neural networks (Computer science)
Intelligent systems
Ischemia Stroke > Diagnosis
Biomedical engineering
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7304
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spelling my.unimap-73042010-01-15T13:58:16Z Early diagnosis of Ischemia Stroke using neural network Anita, Thakur Surekha, Bhanot Mishra, S.N. anni.thakur@gmail.com Feed forward neural network MATLAB Cerebral ischemia stroke Neural networks (Computer science) Intelligent systems Ischemia Stroke -- Diagnosis Biomedical engineering Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. Technological and computing evolution promoted new opportunities to improve the quality of life, in particular, the quality of early detection of acute disease. Many intelligent systems have been developed with the purpose of enhancing health-care and providing better health care facilities at reduced cost. Artificial Intelligent techniques are indeed worth exploring and integrating in the medical system for diagnosis, prediction and prescription. The aim of this paper is to determine a noninvasive method that the general population can easily use to detect whether a patient has cerebral ischemia stroke. The problem addressed in this paper is prediction of possibility of cerebral ischemia and it is estimated from symptoms and risk factors given by the patients. Exactly early prognosis of cerebral ischemia stroke has practical importance in medicine. A feed forward neural network with back propagation was used for decision of cerebral ischemia stroke prediction. Developed Neural network model with appropriate training provides an accuracy of 99.99%. 2009-11-17T03:08:24Z 2009-11-17T03:08:24Z 2009-10-11 Working Paper p.2B10 1 - 2B10 5 http://hdl.handle.net/123456789/7304 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Feed forward neural network
MATLAB
Cerebral ischemia stroke
Neural networks (Computer science)
Intelligent systems
Ischemia Stroke -- Diagnosis
Biomedical engineering
spellingShingle Feed forward neural network
MATLAB
Cerebral ischemia stroke
Neural networks (Computer science)
Intelligent systems
Ischemia Stroke -- Diagnosis
Biomedical engineering
Anita, Thakur
Surekha, Bhanot
Mishra, S.N.
Early diagnosis of Ischemia Stroke using neural network
description Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
author2 anni.thakur@gmail.com
author_facet anni.thakur@gmail.com
Anita, Thakur
Surekha, Bhanot
Mishra, S.N.
format Working Paper
author Anita, Thakur
Surekha, Bhanot
Mishra, S.N.
author_sort Anita, Thakur
title Early diagnosis of Ischemia Stroke using neural network
title_short Early diagnosis of Ischemia Stroke using neural network
title_full Early diagnosis of Ischemia Stroke using neural network
title_fullStr Early diagnosis of Ischemia Stroke using neural network
title_full_unstemmed Early diagnosis of Ischemia Stroke using neural network
title_sort early diagnosis of ischemia stroke using neural network
publisher Universiti Malaysia Perlis
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7304
_version_ 1643788753630658560
score 13.222552

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