Performance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiers

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dc.contributor.authorBaşarslan, Muhammet Sinan
dc.contributor.authorKayaalp, Fatih
dc.date.accessioned2020-04-30T23:20:42Z
dc.date.available2020-04-30T23:20:42Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionInternational Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEYen_US
dc.descriptionBASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169en_US
dc.descriptionWOS: 000491430200016en_US
dc.description.abstractTechnological developments generally have positive effects on our daily lives especially on health domain. Diagnosing diseases through new machines or methods are easier than compared to the past. Benchmarking the effect of attribute selection methods on the performance of classification algorithms in a study to diganose the chronic kidney disease (CKD) by using classification algorithms are aimed. Data set on CKD taken from the UCI machine learning repository has been used for the experiments. After a variety of pre-processing, normalization and attribute selection processes, classifier models are designed. In order to determine the attributes that have gerater contribution on the classification results, the Correlation Based attribute selection (CBAS) method and Fuzzy Rough Set Based attribute selection (FRSBAS) method were used. Two data sets obtained by each attribute selection method and the raw data are classified by 4 classifiers including k-Nearest Neighbor, Navie Bayes, Random Forest and Logistic Regression. The test and training data are separated by 5-fold cross validation. The accuracy, precision, sensitivity, ROC curve and F-measure parameters obtained from confusion matrix are used to compare and evaluate the results of the models. As a result of the study, it is seen that the application of FRSBAS method on CKD data set performs better in all classification algorithms.en_US
dc.description.sponsorshipIEEE Turkey Sect, IEEE EMB, Erasmus+, Europassen_US
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4061
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 Scientific Meeting On Electrical-Electronics & Biomedical Engineering And Computer Science (Ebbt)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy logicen_US
dc.subjectclassificationen_US
dc.subjectchronic kidney diseaseen_US
dc.subjectdata miningen_US
dc.subjectattribute selectionen_US
dc.titlePerformance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiersen_US
dc.typeConference Objecten_US

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