A machine learning approach to predict creatine kinase test results
| dc.authorid | 0000-0001-8359-5713 | |
| dc.authorid | 0000-0001-8863-8348 | |
| dc.authorid | 0000-0002-5971-9218 | |
| dc.contributor.author | Canbolat, Zehra Nur | |
| dc.contributor.author | Silahtaroğlu, Gökhan | |
| dc.contributor.author | Doğuç, Özge | |
| dc.contributor.author | Yılmaztürk, Nevin | |
| dc.date.accessioned | 2020-12-18T10:50:25Z | |
| dc.date.available | 2020-12-18T10:50:25Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü | |
| dc.description.abstract | Most 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. | |
| dc.identifier.citation | Canbolat, 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-2020-01231 | |
| dc.identifier.doi | 10.28991/esj-2020-01231 | |
| dc.identifier.endpage | 296 | |
| dc.identifier.issn | 2610-9182 | |
| dc.identifier.issue | 4 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 283 | |
| dc.identifier.uri | https://dx.doi.org/10.28991/esj-2020-01231 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/6099 | |
| dc.identifier.volume | 4 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Ital Publication | |
| dc.relation.ispartof | Emerging Science Journal | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | Creatine Kinase | |
| dc.subject | Data Mining | |
| dc.subject | Decision Tree | |
| dc.subject | Laboratory Tests | |
| dc.subject | Machine Learning | |
| dc.title | A machine learning approach to predict creatine kinase test results | |
| dc.type | Article |
