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ID+: Enhancing medical knowledge acquisition with machine learning

Moustakis Vasilis, Vlachakis Ioannis, Lena Gaga, G. Charissis

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URIhttp://purl.tuc.gr/dl/dias/EEC4D06C-91CB-456C-8B46-2117E9E161EB-
Identifierhttps://doi.org/10.1080/088395196118605 -
Languageen-
TitleID+: Enhancing medical knowledge acquisition with machine learningen
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
CreatorVlachakis Ioannisen
CreatorΒλαχακης Ιωαννηςel
CreatorLena Gagaen
Creator G. Charissis en
PublisherResearchGateen
Content SummaryLearning from patient records may aid medical knowledge acquisition and decision making. Decision tree induction, based on ID3, is a well-known approach of learning from examples. In this article we introduce a new data representation formalism that extends the original ID3 algorithm. We propose a new algorithm, ID+, which adopts this representation scheme. ID+ provides the capability of modeling dependencies between attributes or attribute values and of handling multiple values per attribute. We demonstrate our work via a series of medical knowledge acquisition experiments that are based on a ''real-world'' application of acute abdominal pain in children. In the context of these experiments, we compare ID+ with C4.5, NewId, and a Naive Bayesian classifier. Results demonstrate that the rules acquired via ID+ improve decision tree clinical comprehensibility and complement explanations supported by the Naive Bayesian classifier, while in terms of classification, accuracy decrease is marginal. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-16-
Date of Publication1996-
Bibliographic CitationL. Gaga, V. Moustakis, Y. Vlachakis & G. Charissis, (1996). "ID+: Enhancing Medical Knowledge Acquisition Using Inductive Machine Learning." Applied Artificial Intelligence: An International Journal , Vol. 10, Iss 2, pp 79‐94. DOI: 10.1080/088395196118605 en

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