![Ιδρυματικό Αποθετήριο [SANDBOX]](images/dias.logo.png "Ιδρυματικό Αποθετήριο [SANDBOX]"){kind=logo}
Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/EEC4D06C-91CB-456C-8B46-2117E9E161EB | - |
| Αναγνωριστικό | https://doi.org/10.1080/088395196118605 | - |
| Γλώσσα | en | - |
| Τίτλος | ID+: Enhancing medical knowledge acquisition with machine learning | en |
| Δημιουργός | Moustakis Vasilis | en |
| Δημιουργός | Μουστακης Βασιλης | el |
| Δημιουργός | Vlachakis Ioannis | en |
| Δημιουργός | Βλαχακης Ιωαννης | el |
| Δημιουργός | Lena Gaga | en |
| Δημιουργός | G. Charissis | en |
| Εκδότης | ResearchGate | en |
| Περίληψη | 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. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-16 | - |
| Ημερομηνία Δημοσίευσης | 1996 | - |
| Βιβλιογραφική Αναφορά | 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 | en |
Copyright © DIAS 2013
