The Efficiency of Classification Techniques in Predicting Anemia Among Children: A Comparative Study

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dc.authoridSonuc, Emrullah/0000-0001-7425-6963
dc.contributor.authorSaihood, Qusay
dc.contributor.authorSonuc, Emrullah
dc.date.accessioned2024-09-29T15:50:52Z
dc.date.available2024-09-29T15:50:52Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description1st International Conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC) -- JUN 06-08, 2021 -- ELECTR NETWORKen_US
dc.description.abstractAnemia is the most common disease among children under school age, especially in developing countries, due to a lack of understanding about its causes and preventive measures. In most cases, anemia refers to malnutrition and is closely related to demographic and social factors. Previously, statistical methods were used to predict anemia among children and identify associated factors. It was concluded that this is not a good way. Following the success of machine learning (ML) techniques in exploring knowledge from clinical data in healthcare, it was a good chance to explore the knowledge of social factors associated with childhood anemia. In this study, we compared the performance of eight different ML techniques for predicting anemia in children using social factors to find the most appropriate method. ML techniques achieved promising results in predicting and identifying factors associated with childhood anemia. Multilayer perceptron (MLP) has the best accuracy of 81.67% with all features, while Decision Tree (DT) has the best accuracy of 82.50% when we applied feature selection methods. The explored knowledge of the social factors associated with anemia can guide nutritional practices and factors essential to child health. Additionally, identified factors can help prevent anemia outbreaks for appropriate intervention by governments and healthcare organizations.en_US
dc.description.sponsorshipAl Maarif Univ Coll,Al Iraqia Univ,Salahaddin Univ,Tishk Int Univ,Appl Comp Res Laben_US
dc.identifier.doi10.1007/978-3-030-97255-4_12
dc.identifier.endpage181en_US
dc.identifier.isbn978-3-030-97255-4
dc.identifier.isbn978-3-030-97254-7
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.scopus2-s2.0-85127679100en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage167en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-97255-4_12
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3774
dc.identifier.wosWOS:000790816200012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofEmerging Technology Trends in Internet of Things and Computing, Tiotc 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectClassificationen_US
dc.subjectAnemia in childrenen_US
dc.subjectIron deficiencyen_US
dc.subjectSocial factorsen_US
dc.titleThe Efficiency of Classification Techniques in Predicting Anemia Among Children: A Comparative Studyen_US
dc.typeConference Objecten_US

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