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Unearth the hidden supportive information for an intelligent medical diagnostic system
Chao S.; Wong F.
Conference Name4th International Workshop on Hybrid Artificial Intelligence Systems
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5572 LNAI
Conference DateJUN 10-12, 2009
Conference PlaceSalamanca, SPAIN

This paper presents an intelligent diagnostic supporting system - i DiaKAW (Intelligent and Interactive Diagnostic Knowledge Acquisition Workbench), which automatically extracts useful knowledge from massive medical data to support real medical diagnosis. In which, our two novel pre-processing algorithms MIDCA (Multivariate Interdependent Discretization for Continuous-valued Attributes) and LUIFS (Latent Utility of Irrelevant Feature Selection) for continuous feature discretization (CFD) and feature selection (FS) respectively, assist in accelerating the diagnostic accuracy by taking the attributes' supportive relevance into consideration during the data preparation process. Such strategy minimizes the information lost and maximizes the intelligence and accuracy of the system. The empirical results on several real-life datasets from UCI repository demonstrate the goodness of our diagnostic system. © 2009 Springer Berlin Heidelberg.

KeywordData Pre-processing Discretization And Feature Selection Intelligent Diagnostic System Latent Supportive Relevance Medical Data Mining
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000267794600048
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Chao S.,Wong F.. Unearth the hidden supportive information for an intelligent medical diagnostic system[C],2009:401-408.
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