| Title: | Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning |
| Journal: | Scientific Reports |
| Published: | 7 Sep 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36071092/ |
| DOI: | https://doi.org/10.1038/s41598-022-19542-5 |
| URL: | https://www.nature.com/articles/s41598-022-19542-5.pdf |
Publication 9828
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Abstract
Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls (n = 14,927) from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables (loneliness/social isolation and depression) and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35-53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls.</p>
7 Authors
- Scott D. Tagliaferri
- Tim Wilkin
- Maia Angelova
- Bernadette M. Fitzgibbon
- Patrick J. Owen
- Clint T. Miller
- Daniel L. Belavy
1 Application
| Application ID | Title |
|---|---|
| 55843 | Localising clusters of pain contributors in chronic back pain with machine learning techniques |
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