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Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children

Moustakis Vasilis, Charissis G., Blazadonakis M

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URIhttp://purl.tuc.gr/dl/dias/97645418-23D8-4D44-802B-C260BA83CB62-
Identifierhttp://www.ncbi.nlm.nih.gov/pubmed/8985539-
Languageen-
TitleDeep assessment of machine learning techniques using patient treatment in acute abdominal pain in childrenen
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
CreatorCharissis G.en
CreatorBlazadonakis Men
PublisherElsevieren
Content SummaryLearning from patient records may aid knowledge acquisition and decision making. Existing inductive machine learning (ML) systems such us NewId, CN2, C4.5 and AQ15 learn from past case histories using symbolic and/or numeric values. These systems learn symbolic rules (IF... THEN like) which link an antecedent set of clinical factors to a consequent class or decision. This paper compares the learning performance of alternative ML systems with each other and with respect to a novel approach using logic minimization, called LML, to learn from data. Patient cases were taken from the archives of the Paediatric Surgery Clinic of the University Hospital of Crete, Heraklion, Greece. Comparison of ML system performance is based both on classification accuracy and on informal expert assessment of learned knowledge.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 CitationM. Blazadonakis, V. Moustakis & G. Charissis, (1996). "Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children." Artificial Intelligence in Medicine, Vol. 8, Iss 6, pp, 527‐542, URL:http://www.ncbi.nlm.nih.gov/pubmed/8985539en

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