A meta-classifier approach for medical diagnosis

Tsirogiannis, GL; Frossyniotis, D; Nikita, KS; Stafylopatis, A

dc.contributor.author	Tsirogiannis, GL	en
dc.contributor.author	Frossyniotis, D	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:42:24Z
dc.date.available	2014-03-01T02:42:24Z
dc.date.issued	2004	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30986
dc.subject	Diagnosis	en
dc.subject	Machine learning	en
dc.subject	Neural networks	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Algorithms	en
dc.subject.other	Computerized tomography	en
dc.subject.other	Data acquisition	en
dc.subject.other	Data reduction	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Learning systems	en
dc.subject.other	Problem solving	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Tumors	en
dc.subject.other	Data collection	en
dc.subject.other	Data sets	en
dc.subject.other	Hepatic lesions	en
dc.subject.other	Meta-classifiers	en
dc.subject.other	Neural networks	en
dc.title	A meta-classifier approach for medical diagnosis	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-24674-9_17	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-24674-9_17	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	Single classifiers, such as Neural Networks, Support Vector Machines, Decision Trees and other, can be used to perform classification of data for relatively simple problems. For more complex problems, combinations of simple classifiers can significantly improve performance. There are several combination methods, like Bagging and Boosting that combine simple classifiers. We propose, here, a new meta-classifier approach which combines several different combination methods, in analogy to the combination of simple classifiers. The meta-classifier approach is employed in the implementation of a medical diagnosis system and evaluated using three benchmark diagnosis problems as well as a problem concerning the classification of hepatic lesions from computed tomography (CT) images.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/978-3-540-24674-9_17	en
dc.identifier.isi	ISI:000221610800017	en
dc.identifier.volume	3025	en
dc.identifier.spage	154	en
dc.identifier.epage	163	en