Diagnosis of gastric carcinoma by classification on feature projections

dc.citation.epage340en_US
dc.citation.issueNumber3en_US
dc.citation.spage231en_US
dc.citation.volumeNumber31en_US
dc.contributor.authorGüvenir, H. A.en_US
dc.contributor.authorEmeksiz, N.en_US
dc.contributor.authorİkizler, N.en_US
dc.contributor.authorÖrmeci, N.en_US
dc.date.accessioned2016-02-08T10:26:30Z
dc.date.available2016-02-08T10:26:30Zen_US
dc.date.issued2004en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractA new classification algorithm, called benefit maximizing classifier on feature projections (BCFP), is developed and applied to the problem of diagnosis of gastric carcinoma. The domain contains records of patients with known diagnosis through gastroscopy results. Given a training set of such records, the BCFP classifier learns how to differentiate a new case in the domain. BCFP represents a concept in the form of feature projections on each feature dimension separately. Classification in the BCFP algorithm is based on a voting among the individual predictions made on each feature. In the gastric carcinoma domain, a lesion can be an indicator of one of nine different levels of gastric carcinoma, from early to late stages. The benefit of correct classification of early levels is much more than that of late cases. Also, the costs of wrong classifications are not symmetric. In the training phase, the BCFP algorithm learns classification rules that maximize the benefit of classification. In the querying phase, using these rules, the BCFP algorithm tries to make a prediction maximizing the benefit. A genetic algorithm is applied to select the relevant features. The performance of the BCFP algorithm is evaluated in terms of accuracy and running time. The rules induced are verified by experts of the domain. © 2004 Elsevier B.V. All rights reserved.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:26:30Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2004en_US
dc.identifier.doi10.1016/j.artmed.2004.03.003en_US
dc.identifier.issn0933-3657en_US
dc.identifier.issn1873-2860en_US
dc.identifier.urihttp://hdl.handle.net/11693/24260en_US
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.artmed.2004.03.003en_US
dc.source.titleArtificial Intelligence in Medicineen_US
dc.subjectBenefit maximizationen_US
dc.subjectFeature projectionen_US
dc.subjectGastric carcinomaen_US
dc.subjectMachine learningen_US
dc.subjectVotingen_US
dc.subjectDiagnosisen_US
dc.subjectDigestive systemen_US
dc.subjectGenetic algorithmsen_US
dc.subjectProblem solvingen_US
dc.subjectTumorsen_US
dc.subjectCarcinomaen_US
dc.subjectFeature projectionen_US
dc.subjectFeature extractionen_US
dc.subjectalgorithmen_US
dc.subjectarticleen_US
dc.subjectcancer classificationen_US
dc.subjectcancer diagnosisen_US
dc.subjectcosten_US
dc.subjectgastroscopyen_US
dc.subjectmathematical computingen_US
dc.subjectmedical recorden_US
dc.subjectpredictionen_US
dc.subjectpriority journalen_US
dc.subjectstomach carcinomaen_US
dc.subjectAdolescenten_US
dc.subjectAdulten_US
dc.subjectAgeden_US
dc.subjectAged, 80 and overen_US
dc.subjectAlgorithmsen_US
dc.subjectChilden_US
dc.subjectChild, Preschoolen_US
dc.subjectDiagnosis, Computer-Assisteden_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectInfanten_US
dc.subjectMaleen_US
dc.subjectMiddle Ageden_US
dc.subjectStomach Neoplasmsen_US
dc.titleDiagnosis of gastric carcinoma by classification on feature projectionsen_US
dc.typeArticleen_US

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