BP Neural Network Model for early Diagnosis of Kawasaki Disease

Jiang Huang; Jian Feng Chen

doi:10.4028/www.scientific.net/AMR.468-471.723

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 468-471BP Neural Network Model for early Diagnosis of...

BP Neural Network Model for early Diagnosis of Kawasaki Disease

Abstract:

In order to diagnose Kawasaki Disease during early phase, clinical symptoms (temperature, rash, conjunctival injection, erythema of thelips, and oral mucosal changes) and laboratory data (white blood cell, neutrophil, platelet, high sensitive c-reactive protein, and erythrocyte sedimentation rate) of 138 children with Kawasaki disease or infectious diseases were used to develop a BP neural network model. 90 random cases were trained using MATLAB software for setting up the BP neural network model. The other 48 cases were analyzed to predict Kawasaki disease using this model. Results showed that the predict accuracy in patients with Kawasaki disease and children with infectious diseases are 95.6% and 88%, respectively. Our result indicates that the BP neural network model is likely to provide an accurate test for early diagnosis of Kawasaki disease.

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Advanced Materials Research (Volumes 468-471)

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723-726

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https://doi.org/10.4028/www.scientific.net/AMR.468-471.723

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Online since:

February 2012

Authors:

Jiang Huang, Jian Feng Chen

Keywords:

Back Propagation, Diagnosis, Kawasaki Disease

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