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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
