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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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