Medical Prognosis Generation in Blood Total Test Results

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dc.contributor.authorKim, You Jin-
dc.contributor.authorHyeon, Jonghwan-
dc.contributor.authorOh, Kyo Joong-
dc.contributor.authorChoi, Ho Jin-
dc.date.accessioned2017-01-16T01:11:15Z-
dc.date.available2017-01-16T01:11:15Z-
dc.date.created2017-01-03-
dc.date.issued2016-12-08-
dc.identifier.citationThe 29th anniversary of the Australasian Joint Conference on Artificial Intelligence-
dc.identifier.urihttp://hdl.handle.net/10203/219342-
dc.description.abstractIn this paper, we present two approaches to generate prognosis from general blood test results. The first approach is a knowledge-based approach using ripple-down rules (RDR). The knowledge-based approach with RDR converts knowledge of pathologists into a knowl- edge base with the minimum intervention of knowledge engineers. The second approach is a machine-learning(ML)-based approach using decision tree, random forest and deep neural network (DNN). The ML-based approach learns patterns of attributes from various cases of general blood test. Our experimental results show that there are indeed some important patterns of the attributes in general blood test results, and they are adequately encoded by the both approaches.-
dc.languageEnglish-
dc.publisherSchool of Engineering and ICT University of Tasmania-
dc.titleMedical Prognosis Generation in Blood Total Test Results-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameThe 29th anniversary of the Australasian Joint Conference on Artificial Intelligence-
dc.identifier.conferencecountryAT-
dc.identifier.conferencelocationHobart, Tasmania-
dc.contributor.localauthorOh, Kyo Joong-
dc.contributor.localauthorChoi, Ho Jin-
dc.contributor.nonIdAuthorKim, You Jin-
dc.contributor.nonIdAuthorHyeon, Jonghwan-

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