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dc.contributor.authorCanbolat, Zehra Nur
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorDoğuç, Özge
dc.contributor.authorYılmaztürk, Nevin
dc.date.accessioned2020-12-18T10:50:25Z
dc.date.available2020-12-18T10:50:25Z
dc.date.issued2020en_US
dc.identifier.citationCanbolat, Z. N., Silahtaroğlu, G., Doğuç, Ö. ve Yılmaztürk, N. (2020). A machine learning approach to predict creatine kinase test results. Emerging Science Journal, 4(4), 283-296. https://dx.doi.org/10.28991/esj-2020-01231en_US
dc.identifier.issn2610-9182
dc.identifier.urihttps://dx.doi.org/10.28991/esj-2020-01231
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6099
dc.description.abstractMost of the research done in the literature are based on statistical approaches and used for deriving reference limits based on lab results. As more data are available to the researchers, ML methods are more effectively used by the clinicians and practitioners to reduce cost and provide more accurate diagnoses. This study aims to contribute to the medical laboratory processes by providing an automated method in order to predict the lab results accurately by machine learning from the previous test results. All patient data obtained have been anonymized, and a total of 449,471 test results have been used to build an integrated dataset. A total of 107,646 unique patients’ data has been used. This study aims to predict the value range of the Creatine Kinase tests, which are taken in separate tubes and usually needs more processing time than the other tests do. Using the lab results and the Random Forest Algorithm, this study reports that the outcome of the Creatine Kinase test can be determined with 97% accuracy by using the AST and ALT test values. This is an important achievement for the practitioners and the patients, as this study submits significant reduction in Creating Kinase test evaluation time.en_US
dc.language.isoengen_US
dc.publisherItal Publicationen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCreatine Kinaseen_US
dc.subjectData Miningen_US
dc.subjectDecision Treeen_US
dc.subjectLaboratory Testsen_US
dc.subjectMachine Learningen_US
dc.titleA machine learning approach to predict creatine kinase test resultsen_US
dc.typearticleen_US
dc.relation.ispartofEmerging Science Journalen_US
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.authorid0000-0001-8359-5713en_US
dc.authorid0000-0001-8863-8348en_US
dc.authorid0000-0002-5971-9218en_US
dc.identifier.volume4en_US
dc.identifier.issue4en_US
dc.identifier.startpage283en_US
dc.identifier.endpage296en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.28991/esj-2020-01231en_US
dc.identifier.scopusqualityQ1en_US


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