Evaluation and measuring classifiers of diabetes diseases

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dc.contributor.authorJasim, Ihsan Salman
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorShaker, Khalid
dc.contributor.authorAbed, Baraa M.
dc.contributor.authorSaleh, Hadeel M.
dc.date.accessioned2021-05-15T12:36:53Z
dc.date.available2021-05-15T12:36:53Z
dc.date.issued2017
dc.departmentFen Bilimleri Enstitüsüen_US
dc.descriptionInternational Conference on Engineering and Technology (ICET) -- AUG 21-23, 2017 -- Akdeniz Univ, Antalya, TURKEY
dc.descriptionDuru, Adil Deniz/0000-0003-3014-9626
dc.description.abstractClassification plays tremendous role in data mining process, especially for huge amount of data and it is suitable for predict new knowledge and discover patterns. This process can work with different types of data whether it was nominal or continuous. In this paper classification will be performs on diseases diagnoses by choosing to work with (k-nearest neighborhood algorithm KNN) measure and evaluate the method with (Artificial Neural Network ANN). These two classification methods have been chosen to classify (Pima-Indian-Diabetes PID) using spiral spinning technique. Classification done by taking 1 to 50 values of (K) in KNN versus 1 to 50 values of hidden layers for ANN in single iteration checking the accuracy as measuring to evaluate performance. T-test used to validate choosing two different factors (K in KNN and number of hidden layers in ANN), t-test results shows that the method is extremely statically significant. After performing classification by changing architecture, ANN proves better results than KNN in this disease classification.en_US
dc.description.sponsorshipIARES, IEEEen_US
dc.identifier.isbn978-1-5386-1949-0
dc.identifier.issn2380-9345
dc.identifier.scopus2-s2.0-85047741526
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/426
dc.identifier.wosWOS:000454987100027
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorJasim, Ihsan Salman
dc.language.isoen
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Conference on Engineering and Technology (Icet)
dc.relation.ispartofseriesInternational Conference on Engineering and Technology
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData Miningen_US
dc.subjectClassificationen_US
dc.subjectDisease Diagnosingen_US
dc.subjectArtificial Neural Networken_US
dc.subjectk-Nearest Neighborhooden_US
dc.titleEvaluation and measuring classifiers of diabetes diseases
dc.typeConference Object

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