A high efficiency thyroid disorders prediction system with non-dominated sorting genetic algorithm NSGA-II as a feature selection algorithm

Basit Öğe Kaydı
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorMohammed, Mohammed Sami
dc.contributor.authorMohammed, Sahar Jasim
dc.date.accessioned2021-05-15T12:49:43Z
dc.date.available2021-05-15T12:49:43Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.description2020 International Conference for Emerging Technology, INCET 2020 -- 5 June 2020 through 7 June 2020 -- -- 162255
dc.description.abstractIn spite of availability of patient's data in hospitals, health care institute and websites but still hard to collected especially for a risk disease like thyroid disorders. A new model by using Non Sorting Genetic Algorithm are selected for rows reductions and attributes selected with a three data mining techniques for a faster and accurate thyroid disorders detection. Two types of thyroid disorders with 4 different classes for each type are used for this design, in addition 500+972 are used with 29 attributes as training and testing data respectively with cross validation=5. Performances of this model are measured by using some parameter as accuracy , precision , etc. This model is studied for using all/some features with the proposed model and compare it with Sequential model. A scatter plot and area under curve are also presented in this work for training data to show the classes predication enhancement. © 2020 IEEE.en_US
dc.identifier.doi10.1109/INCET49848.2020.9154189
dc.identifier.isbn9781728162218
dc.identifier.scopus2-s2.0-85090570965
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/INCET49848.2020.9154189
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1103
dc.indekslendigikaynakScopus
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 International Conference for Emerging Technology, INCET 2020
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCross Validationen_US
dc.subjectDecision Treeen_US
dc.subjectNon Dominated Sorting Genetic Algorithm (NSGA-II)en_US
dc.subjectSequential Modelen_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.subjectThyroid Disordersen_US
dc.titleA high efficiency thyroid disorders prediction system with non-dominated sorting genetic algorithm NSGA-II as a feature selection algorithm
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu