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Medical knowledge discovery from a regional asthma dataset
journal contribution
posted on 2008-01-01, 00:00 authored by S Schmidt, Gang LiGang Li, Yi-Ping Phoebe ChenPaediatric asthma represents a significant public health problem. To date, clinical data sets have typically been examined using traditional data analysis techniques. While such traditional statistical methods are invariably widespread, large volumes of data may overwhelm such approaches. The new generation of knowledge discovery techniques may therefore be a more appropriate means of analysis. The primary purpose of this study was to investigate an asthma data set, with the application of various data mining techniques for knowledge discovery. The current study utilises data from an asthma data set (n ≈ 17000). The findings revealed a number of factors and patterns of interest.
History
Journal
Lecture notes in computer scienceVolume
5227Pagination
888 - 895Publisher
Springer Berlin / HeidelbergLocation
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2008, Springer-Verlag Berlin HeidelbergUsage metrics
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