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dc.contributor.authorBabaee, Mahsa
dc.contributor.authorSoleimani, Paria
dc.contributor.authorZali, Alireza
dc.contributor.authorMohseni Kabir, Nima
dc.contributor.authorChizari, Mahmoud
dc.date.accessioned2019-07-16T01:16:38Z
dc.date.available2019-07-16T01:16:38Z
dc.date.issued2017-10-08
dc.identifier.citationBabaee , M , Soleimani , P , Zali , A , Mohseni Kabir , N & Chizari , M 2017 , ' A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery ' , International Clinical Neuroscience Journal (ICNJ) , vol. 4 , no. 4 , pp. 143-151 . https://doi.org/10.15171/icnj.2017.05
dc.identifier.otherBibtex: urn:a0aa9c8c9dd644f7daf56c923342a5b9
dc.identifier.otherORCID: /0000-0003-0555-1242/work/62751491
dc.identifier.urihttp://hdl.handle.net/2299/21433
dc.description.abstractBackground: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].en
dc.format.extent9
dc.format.extent1078208
dc.language.isoeng
dc.relation.ispartofInternational Clinical Neuroscience Journal (ICNJ)
dc.titleA Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgeryen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionEnergy and Sustainable Design Research Group
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionCentre for Climate Change Research (C3R)
dc.contributor.institutionCentre for Future Societies Research
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.15171/icnj.2017.05
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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