Classifying anemia types using artificial learning methods

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dc.contributor.authorYildiz, Tuba Karagul
dc.contributor.authorYurtay, Nilufer
dc.contributor.authoronec, Birgul
dc.date.accessioned2021-12-01T18:47:57Z
dc.date.available2021-12-01T18:47:57Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThe most common blood disease worldwide is anemia, defined by the World Health Organization as a condition in which the red blood cell count or oxygen-carrying capacity is insufficient. As both a disease and a symptom, this condition affects the quality of life. Early and correct diagnosis of the type of anemia is vital in terms of patient treatment. The increasing number of patients and hospital priorities, as well as difficulties in reaching medical specialists, may impede such a diagnosis. The present work proposes a system that will enable the recognition of anemia under general clinical practice conditions. For this system, a model constructed using four different artificial learning methods. Artificial Neural Networks, Support Vector Machines, Naive Bayes, and Ensemble Decision Tree methods are used as classification algorithms. The models are evaluated with a dataset of 1663 samples and used 25 attributes, including hemogram data and general information such as age, sex, chronic diseases, and symptoms to diagnose 12 different anemia types. Data are collected by examining patient files at a university hospital in Turkey. In addition to all the data used by the doctors, the model also utilized eight different datasets created via particular feature selection techniques. The interface is designed to provide decision support to both medical consultants and medical students. Data are classified using the four different algorithms and an acceptable success ratio is obtained for each. Each model is validated using Classification Error, Area Under Curve, Precision, Recall, and F-score metrics in addition to Accuracy values. The highest accuracy (85.6%) achieved using Bagged Decision Trees, followed by Boosted Trees (83.0%) and Artificial Neural Networks (79.6%). (C) 2020 Karabuk University. Publishing services by Elsevier B.V.en_US
dc.description.sponsorshipResearch Fund of Sakarya University, Turkey [2015-50-02-010]en_US
dc.description.sponsorshipThis work was supported by the Research Fund of Sakarya University, Turkey, under Project Number: 2015-50-02-010. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.doi10.1016/j.jestch.2020.12.003
dc.identifier.endpage70en_US
dc.identifier.issn2215-0986
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85098985234en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage50en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2020.12.003
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10417
dc.identifier.volume24en_US
dc.identifier.wosWOS:000615232500006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Science And Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnemiaen_US
dc.subjectArtificial neural networken_US
dc.subjectDecision treeen_US
dc.subjectMedical diagnosisen_US
dc.subjectNaive Bayesen_US
dc.subjectSupport vector machineen_US
dc.titleClassifying anemia types using artificial learning methodsen_US
dc.typeArticleen_US

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