Compensatory Neurofuzzy Model for Discrete Data Classification in Biomedical

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dc.contributor.authorCeylan, Rahime
dc.date.accessioned2020-03-26T19:01:34Z
dc.date.available2020-03-26T19:01:34Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Graphic and Image Processing (ICGIP) -- OCT 24-26, 2014 -- Beijing, PEOPLES R CHINAen_US
dc.description.abstractBiomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.en_US
dc.description.sponsorshipInt Assoc Comp Sci & Informat Technol, Wuhan Univen_US
dc.identifier.doi10.1117/12.2179960en_US
dc.identifier.isbn978-1-62841-558-2
dc.identifier.issn0277-786Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1117/12.2179960
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31962
dc.identifier.volume9443en_US
dc.identifier.wosWOS:000354613300089en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen_US
dc.relation.ispartofSIXTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2014)en_US
dc.relation.ispartofseriesProceedings of SPIE
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCompensatory neurofuzzy networken_US
dc.subjectdiscrete data classificationen_US
dc.subjectWisconsin Breast Cancer dataseten_US
dc.subjectPima India Diabetes dataseten_US
dc.titleCompensatory Neurofuzzy Model for Discrete Data Classification in Biomedicalen_US
dc.typeConference Objecten_US

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