Diagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization

dc.contributor.authorAdem, Kemal
dc.date.accessioned2025-01-21T14:26:40Z
dc.date.available2025-01-21T14:26:40Z
dc.date.issued2018
dc.description.abstractLate diagnosis of chronic kidney disease, adisease that has increased in recent years and threatens human life, may leadto dialysis or kidney failure. In this study, kNN, SVM, RBF and Random subspacedata mining methods were applied on the data set consisting of 400 samples and24 attributes taken from UCI for classification of chronic kidney disease with particleswarm optimization (PSO) based feature selection method. As a result of thestudy, the results of the application of each data mining method are comparedwith the resultant training and test results. As a result of the comparison, itwas seen that the method of PSO feature selection affects the classificationsuccess positively. Moreover, as a method of data mining, it has been seen thatthe random subspace method has higher accuracy rates than the other methods.
dc.identifier.dergipark472881
dc.identifier.doi10.29137/umagd.472881
dc.identifier.issn1308-5514
dc.identifier.issue3-1
dc.identifier.startpage5
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/614668
dc.identifier.urihttps://dergipark.org.tr/tr/pub/umagd/issue/42019/472881
dc.identifier.urihttps://doi.org/10.29137/umagd.472881
dc.identifier.urihttps://hdl.handle.net/20.500.12587/19979
dc.identifier.volume1
dc.language.isoen
dc.publisherKırıkkale Üniversitesi
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectChronic kidney disease
dc.subjectParticle Swarm Optimization
dc.subjectRandom Subspace
dc.titleDiagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization
dc.typeArticle

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