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Title
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Towards data-driven biopsychosocial classification of non-specific chronic low back pain : a pilot study
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Author
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Abstract
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| The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naive Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings. |
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Language
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English
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Source (journal)
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Scientific reports. - London, 2011, currens
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Publication
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London
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Nature Publishing Group
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2023
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ISSN
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2045-2322
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DOI
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10.1038/S41598-023-40245-Y
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Volume/pages
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13
:1
(2023)
, p. 1-17
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Article Reference
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13112
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ISI
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001049345000022
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Pubmed ID
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37573418
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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