Fuzzy Logic and Correlation-Based Hybrid Classification on Hepatitis Disease Data Set

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dc.authoridBAKIR, Huseyin/0000-0001-5473-5158
dc.authoridBASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
dc.authorwosidBASARSLAN, MUHAMMET SINAN/W-2030-2018
dc.contributor.authorBasarslan, M. Sinan
dc.contributor.authorBakir, H.
dc.contributor.authorYucedag, I
dc.date.accessioned2021-12-01T18:48:34Z
dc.date.available2021-12-01T18:48:34Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.descriptionInternational Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYen_US
dc.description.abstractDevelopments in the health field are closely affecting humanity. The development of information technologies increases this effect. In this study, it was aimed to help the decision makers by increasing the accuracy rate in the detection of hepatitis disease. The data set was obtained from UCI machine learning source. Data preprocessing, attribute selection and classifier models were established on this data set, respectively. After the deficiency in the data of the patients with hepatitis was normalized, correlation-based and fuzzy-based rough force attribute selection methods were applied and the attributes that contributed to the classification were selected. The hepatitis dataset and the data set formed by the attributes determined by the correlation-based and the fuzzy-based rough-attribute selection methods were classified using the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression algorithms and the results were compared. Accuracy, sensitivity precision, ROC curve and F-measure values were used in the comparison of classification algorithms. In the process of separating the data set as a test and training set, a 5-fold cross-validation method was applied. It has been observed that the fuzzy rough clustering algorithm is more successful than the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression classification methods in the detection of hepatitis disease.en_US
dc.identifier.doi10.1007/978-3-030-36178-5_68
dc.identifier.endpage800en_US
dc.identifier.isbn978-3-030-36178-5; 978-3-030-36177-8
dc.identifier.issn2367-4512
dc.identifier.scopus2-s2.0-85083450204en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage787en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-36178-5_68
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10562
dc.identifier.volume43en_US
dc.identifier.wosWOS:000678771000068en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofArtificial Intelligence And Applied Mathematics In Engineering Problemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectData miningen_US
dc.subjectFuzzy logicen_US
dc.subjectHepatitis diseaseen_US
dc.titleFuzzy Logic and Correlation-Based Hybrid Classification on Hepatitis Disease Data Seten_US
dc.typeConference Objecten_US

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