Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1645
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dc.contributor.authorKoyuncu, Hasan-
dc.date.accessioned2022-01-30T17:32:50Z-
dc.date.available2022-01-30T17:32:50Z-
dc.date.issued2021-
dc.identifier.isbn9781665449304-
dc.identifier.urihttps://doi.org/10.1109/ISMSIT52890.2021.9604687-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1645-
dc.description5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473en_US
dc.description.abstractThe detection of heart failure is a vital and complicated issue that is needed to be analyzed comprehensively. On the basis of medicine, different tests and various scan techniques are utilized to efficiently make a decision. On the basis of machine learning, two phenomena come into prominence: 1-Qalitative data, 2-Framework design to detect the necessary information among the data.In this paper, an efficient framework is proposed to reveal the heart failure on the specific data. Three optimized classifiers were compared to assign the classification unit of framework. Manuel selection and filter based-feature ranking methods were considered to determine the necessary information and to reveal the heart failure. In experiments, two-fold cross validation was utilized as the test method to force the classifiers, and seven metrics based-comparisons were realized to objectively choose the features and classifiers. Consequently, the best framework achieved remarkable scores of 86.62% (accuracy), 83.01% (AUC), 72.92% (sensitivity), 93.10% (specificity), 82.39% (g-mean), 83.33% (precision) and 77.78% (f-measure) for survival prediction on heart failure clinical records. © 2021 IEEE.en_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Konya Technical University's Scientific Research Projects.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2021 - 5th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectchaoticen_US
dc.subjectframework designen_US
dc.subjectgauss mapen_US
dc.subjectheart failureen_US
dc.subjectoptimized classifieren_US
dc.subjectpattern classificationen_US
dc.subjectClassification (of information)en_US
dc.subjectFeature extractionen_US
dc.subjectHearten_US
dc.subjectTestingen_US
dc.subjectChaoticsen_US
dc.subjectFailure detectionen_US
dc.subjectFeature classifiersen_US
dc.subjectFramework designsen_US
dc.subjectGauss mapsen_US
dc.subjectHeart failureen_US
dc.subjectHybrid classifieren_US
dc.subjectOptimized classifieren_US
dc.subjectPatterns classificationen_US
dc.subjectScan techniquesen_US
dc.subjectCardiologyen_US
dc.titleA Framework Design for Heart Failure Detection: Analyzes on Features and Hybrid Classifiersen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/ISMSIT52890.2021.9604687-
dc.identifier.scopus2-s2.0-85123300864en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.startpage62en_US
dc.identifier.endpage67en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid55884277600-
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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