©1996-2011 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal
use. All other usage or distribute on is illegal under international copyright
treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the
publisher. This article is exclusively copyrighted in its entirety to OJB
publications. This article may be copied once but may not be, reproduced or re-transmitted
without the express permission of the editors. This journal satisfies the refereeing requirements
(DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to
this page you must link directly to this page only here rather than put up your
own page.
OJBTM
Online Journal of Bioinformatics ©
Volume 12(2):379-385, 2011
Evaluation of severity
of asthma using artificial neural network
Atul Kumar, Sachidanand
Singh, J. Jannet Vennila
Department
of Bioinformatics, Karunya University, Karunya Nagar, Coimbatore, Tamil Nadu, India
ABSTRACT
Kumar A, Singh S, Vennila JJ., Evaluation
of severity of asthma by artificial neural network, Online J Bioinformatics, 12(2):379-385,
2011. A system to evaluate the potential severity of asthma using
a neural network is described. Peak Expiratory Flow
Rate, Daytime Symptom Frequency, Night time Symptom Frequency, Peak Expiratory
Flow Rate Variability and Oxygen Saturation were used as input in a neural
network diagnostic system using MATLAB. Based on these inputs, severity of asthma
was predicted as an output. Results suggested that the network
response was satisfactory, so the sim function was
applied to the network to use on new inputs. The network was supplied with 10
sets of input and the output was the same.
Keywords: Asthma,
Neural Network, MATLAB, Daytime Symptom Frequency, Night time Symptom Frequency, Peak
Expiratory Flow Rate Variability and Oxygen Saturation.