Prediction of paediatric asthma hospitalisation using data mining techniques

Schmidt, Sam, Li, Gang and Chen, Yi-Ping Phoebe 2008, Prediction of paediatric asthma hospitalisation using data mining techniques, in PRIB 2008 : The 3rd IAPR International Conference on Pattern Recognition in BioInformatics, Springer Berlin / Heidelberg, Berlin, Germany.

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Title Prediction of paediatric asthma hospitalisation using data mining techniques
Author(s) Schmidt, Sam
Li, Gang
Chen, Yi-Ping Phoebe
Conference name Pattern Recognition in Bioinformatics. Conference (3rd : 2008 : Melbourne, Vic)
Conference location Melbourne, Vic.
Conference dates 15-17 October 2008
Title of proceedings PRIB 2008 : The 3rd IAPR International Conference on Pattern Recognition in BioInformatics
Editor(s) Chetty, Madhu
Ahmad, Shandar
Ngom, Alioune
Teng, Shyh Wei
Publication date 2008
Conference series Pattern Recognition in Bioinformatics Conference
Publisher Springer Berlin / Heidelberg
Place of publication Berlin, Germany
Summary Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropolitan areas may overutilise emergency department services. In addition, this study found that the prediction of hospitalisaion for asthma in children was greater for those with a written asthma management plan.
ISBN 9780732622268
3540884343
ISSN 0302-9743
Language eng
Field of Research 080301 Bioinformatics Software
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2008
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018159

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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