Diagnosing Hematological Disorders Using Deep Learning Method

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dc.contributor.authorYurtay, Nilüfer
dc.contributor.authorÖneç, Birgül
dc.contributor.authorKaragül, Tuba
dc.date.accessioned2023-07-26T11:57:18Z
dc.date.available2023-07-26T11:57:18Z
dc.date.issued2021
dc.departmentDÜ, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalıen_US
dc.description.abstractDeciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.en_US
dc.identifier.doi10.35377/saucis.04.02.836375
dc.identifier.endpage243en_US
dc.identifier.issn2636-8129
dc.identifier.issue2en_US
dc.identifier.startpage227en_US
dc.identifier.trdizinid502430en_US
dc.identifier.urihttp://doi.org/10.35377/saucis.04.02.836375
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/502430
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13116
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorÖneç, Birgül
dc.language.isoenen_US
dc.relation.ispartofSakarya University Journal of Computer and Information Sciences (Online)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectanemiaen_US
dc.subjectclassificationen_US
dc.subjectdeep learningen_US
dc.titleDiagnosing Hematological Disorders Using Deep Learning Methoden_US
dc.typeArticleen_US

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