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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

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/97645418-23D8-4D44-802B-C260BA83CB62-
Αναγνωριστικόhttp://www.ncbi.nlm.nih.gov/pubmed/8985539-
Γλώσσαen-
ΤίτλοςDeep assessment of machine learning techniques using patient treatment in acute abdominal pain in childrenen
ΔημιουργόςMoustakis Vasilisen
ΔημιουργόςΜουστακης Βασιληςel
ΔημιουργόςCharissis G.en
ΔημιουργόςBlazadonakis Men
ΕκδότηςElsevieren
ΠερίληψηLearning 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-16-
Ημερομηνία Δημοσίευσης1996-
Βιβλιογραφική ΑναφοράM. 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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