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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-
Αναγνωριστικόhttps://doi.org/10.1080/088395196118605 -
Γλώσσαen-
ΤίτλοςID+: Enhancing medical knowledge acquisition with machine learningen
ΔημιουργόςMoustakis Vasilisen
ΔημιουργόςΜουστακης Βασιληςel
ΔημιουργόςVlachakis Ioannisen
ΔημιουργόςΒλαχακης Ιωαννηςel
ΔημιουργόςLena Gagaen
Δημιουργός G. Charissis en
ΕκδότηςResearchGateen
ΠερίληψηLearning 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-16-
Ημερομηνία Δημοσίευσης1996-
Βιβλιογραφική ΑναφοράL. 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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