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A Multi-Level Biomedical Classification Model By Using Abstraction And Aggregation Techniques
Simon Fong1; Andy Ip1; Sabah Mohammed2
2011-02
Conference NameThe Eighth IASTED International Conference on Biomedical Engineering (Biomed 2011)
Source PublicationProceedings of The Eighth IASTED International Conference on Biomedical Engineering (Biomed 2011)
Conference DateFebruary 16 – 18, 2011
Conference PlaceInnsbruck, Austria
Abstract

Data mining on biomedical data usually faces challenges of preserving privacy and finding associations among the attributes. Comprised of various and meticulous clinical measurements, the data to be data-mined often carry many attributes. When all these attributes are used in constructing a classification model, it may lead to a well-known problem in data mining called over-fitting which results in poor prediction accuracy. At the same time, the high resolution (or details) of the attributes may compromise the privacy of the patients’ identities. In this paper a multi-level classification model is proposed to analyse biomedical data with the attributes flexibly abstracted and aggregated at will of the user. The novel method contributes to biomedical research community in threefold: (1) increasing the prediction accuracy; (2) subsiding the privacy issue; and (3) enabling the relations between the attributes of the data to be further analysed by biomedical experts. The prototype of the model is tested via several experiments with some classical biomedical data obtained from UCI. A visualization tool is also programmed that shows both the significances of the attributes and their predictive powers. The experimental results indicate that by applying appropriate aggregation and abstraction techniques, decision trees can make to be more compact and more accurate.

KeywordBiomedical Data Classication Decision Tree Multi-level Data Visualization
DOIhttp://doi.org/10.2316/P.2011.723-146
URLView the original
Language英语
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Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Info. Science ,University of Macau,Macau SAR
2.Faculty of Science and Technology,University of Macau,Macau SAR
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Andy Ip,Sabah Mohammed. A Multi-Level Biomedical Classification Model By Using Abstraction And Aggregation Techniques[C],2011.
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