An investigation of machine learning algorithms for prediction of lumbar disc herniation

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dc.authoridGoksen, Aysenur/0000-0003-2273-5908
dc.authoridKocaman, Hikmet/0000-0001-5971-7274
dc.authoridYILDIRIM, Hasan/0000-0003-4582-9018
dc.contributor.authorKocaman, Hikmet
dc.contributor.authorYildirim, Hasan
dc.contributor.authorGoeksen, Aysenur
dc.contributor.authorArman, Gokce Merve
dc.date.accessioned2025-03-17T12:27:34Z
dc.date.available2025-03-17T12:27:34Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractThe prevalence of lumbar disc herniation (LDH), which makes patients' daily activities more difficult and reduces their quality of life, has tended to increase recently. Many risk factors associated with LDH have been reported. In this study, LDH was predicted using machine learning techniques using measures of the lumbar paraspinal muscles, lumbar vessels cross-sectional area (CSA), and lumbar sagittal curve. Three hundred and forty-four individuals' MR scans were prospectively enrolled (264 with LDH and 80 healthy). Predictive factors were the lumbar sagittal curve and the cross-sectional areas of the lumbar paraspinal muscles and vessels from sagittal and axial MR images. The measurements have been analyzed via ten different and most common machine learning algorithms by considering a comprehensive parameter tuning and cross-validation process. The variable importance results have been also presented. XGBoost algorithm among all algorithms has provided the best results in terms of different classification metrics including f-score ( 0.830 ), AUC ( 0.939 ), accuracy ( 0.922 ), and kappa ( 0.779 ). The findings of this study demonstrated that cross-sectional areas of the quadratus lumborum and abdominal aorta can be utilized as a reliable indicator of LDH. Consequently, the developed model and the variables found to be important may guide to healthcare professionals to make more accurate and effective decisions in terms of prediction the LDH.
dc.identifier.doi10.1007/s11517-023-02888-x
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.pmid37535298
dc.identifier.scopus2-s2.0-85166641615
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11517-023-02888-x
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2309
dc.identifier.wosWOS:001042649200002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofMedical & Biological Engineering & Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectDisc herniation
dc.subjectMachine learning
dc.subjectMagnetic resonance imaging
dc.subjectDiagnosis
dc.subjectPhysiotherapy
dc.titleAn investigation of machine learning algorithms for prediction of lumbar disc herniation
dc.typeArticle

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