MFDM: MRI FREE DECISION MODEL FOR DIAGNOSIS AND TREATMENT SELECTION IN PATIENTS WITH LOW BACK AND NECK PAIN

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2022-7

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Mimaroğlu Altınay, Beste

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Low back pain (LBP) and neck pain (NP) are public health problems affecting life quality worldwide. Our goal was to develop a machine learning model that can direct LBP and NP patients for the appropriate treatment without magnetic resonance imaging (MRI) findings, thus reducing the demand for MRI and its burden on the health system. Following features from the patient data are analyzed to evaluate the treatment outcomes; demographic information, clinical findings, preoperative evaluation of pain, movement restriction, and pain data duration. Support Vector Machine (SVM) models are built by analyzing ten different attributes from 1482 patient data to classify correct treatment: drug, Radiofrequency (RF)/ Intradiscal Electrothermal Therapy (IDET), or surgical intervention. The stepwise model proposed here classifies drug therapy patients with an 84% success ratio and can direct patients to surgery or RF/IDET with a 74.47% success ratio without MRI results. The proposed MRI Free Decision Model (MFDM) can be utilized in primary healthcare facilities to direct the patients to the appropriate treatment options without MRI, reducing the cost and load on the healthcare system while benefiting the patient by reducing the time to initiate the treatment. There are several guideline recommendations for treating LBP and NP used in various countries, and we expect the proposed MFDM will provide a backbone to form guidelines in Turkey, similar to other guides followed in the EU and USA.

Subject Keywords

Pain Treatment Analysis, Neck and Low Back Pain, Binary SVM, Multi-Class Classification, Burden of MRI

URI

https://hdl.handle.net/11511/98629

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Graduate School of Informatics, Thesis

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Citation Formats

B. Mimaroğlu Altınay, “MFDM: MRI FREE DECISION MODEL FOR DIAGNOSIS AND TREATMENT SELECTION IN PATIENTS WITH LOW BACK AND NECK PAIN,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.