Medical Knowledge Discovery from a Regional Asthma Dataset

Authors:
Sam Schmidt;Gang Li;Yi-Ping Phoebe Chen
Affiliations:
School of Engineering and Information Technology, Deakin University, Vic, Australia 3125;School of Engineering and Information Technology, Deakin University, Vic, Australia 3125;School of Engineering and Information Technology, Deakin University, Vic, Australia 3125
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

Paediatric 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.