Is there a correlation between dominant extremity and cervical disc herniation using machine learning methods?

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dc.authorscopusid15064901400
dc.authorscopusid56033997900
dc.contributor.authorYücel, Engin
dc.contributor.authorAkyuva, Yener
dc.date.accessioned2024-09-19T15:43:39Z
dc.date.available2024-09-19T15:43:39Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractWe aimed to investigate the possibility of determining the relationship between the dominant extremity and cervical disc herniation using a machine learning approach. A total of 561 patients diagnosed with cervical disc herniation were examined for dominant extremity, level and side of cervical disc herniation, and the nature of the herniation (calcified/soft). These patients formed the basis for a two-step machine learning system creation. The first step (included the data of 80% of the patients) focused on determining the type of cervical disc herniation by selecting the top five performing classification models out of 15 different models and tuning the hyperparameters. In the second step, the machine learning system was validated using data from a randomly selected subset of patients (20% of the patients). The study results showed that while most models performed well, the gradient boosting classifier was the most accurate (89.38%) for determining the herniated disc nature. However, for classifying the disc herniation direction, the models did not exhibit strong performance. Thus, machine learning can accurately identify the relationship between cervical disc herniation and dominant extremity with a high degree of accuracy. © 2023 Ondokuz Mayis Universitesi. All rights reserved.en_US
dc.identifier.doi10.52142/omujecm.40.3.35
dc.identifier.endpage639en_US
dc.identifier.issn1309-4483
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85186901249en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage633en_US
dc.identifier.urihttps://doi.org/10.52142/omujecm.40.3.35
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14476
dc.identifier.volume40en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherOndokuz Mayis Universitesien_US
dc.relation.ispartofJournal of Experimental and Clinical Medicine (Turkey)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial intelligenceen_US
dc.subjectdisc degenerationen_US
dc.subjectmachine learningen_US
dc.subjectspinal diseaseen_US
dc.titleIs there a correlation between dominant extremity and cervical disc herniation using machine learning methods?en_US
dc.typeArticleen_US

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