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OJBTM
Online Journal of Bioinformatics ©
Volume
12(2):379-385, 2011.
Evaluation
of severity of asthma by 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.
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