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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/EEC4D06C-91CB-456C-8B46-2117E9E161EB | ||
| Year | 1996 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | L. 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 https://doi.org/10.1080/088395196118605 | ||
| Appears in Collections |
Learning 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.
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