A Hybrid Decision Support System Based on Rough Set and Extreme Learning Machine for Diagnosis of Hepatitis Disease

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dc.contributor.authorKaya, Yılmaz
dc.contributor.authorUyar, Murat
dc.date.accessioned2017-05-08T17:06:39Z
dc.date.available2017-05-08T17:06:39Z
dc.date.issued2014
dc.departmentBelirleneceken_US
dc.description.abstractHepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications.en_US
dc.identifier.scopus2-s2.0-84878164388
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/539
dc.identifier.wosWOS:000321494200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.relation.publicationcategoryUluslararası Hakemli Dergi Makalesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKG_20241224
dc.subjectHepatitis disease; Rough set; Dimensionality reduction; Extreme learning machineen_US
dc.titleA Hybrid Decision Support System Based on Rough Set and Extreme Learning Machine for Diagnosis of Hepatitis Diseaseen_US
dc.typeArticleen_US
dcterms.publisherApplied Soft Computing Journal

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