Abstract
Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Langevin, H. M., and Sherman, K. J., Pathophysiological model for chronic low back pain integrating connective tissue and nervous system mechanisms. Med. Hypotheses 68(1):74–80, 2007.
Gould, D., Kelly, D., Goldstone, L., and Gammon, J., Examining the validity of pressure ulcer risk assessment scales: developing and using illustrated patient simulations to collect data. J. Clin. Nurs. 10:697–706, 2001.
Stener-Victorin, E., Lundeberg, T., Kowalski, J., Opdal, L., Sjöström, J., and Lundeberg, L., Perceptual matching for assessment of itch; reliability and responsiveness analyzed by a rank-invariant statistical method. J. Invest. Dermatol. 121:1301–1305, 2003.
Richter, C. P., and Katz, D. T., Peripheral nerve injuries determined by the skin resistance method. JAMA 122:648, 1943.
Riley, L. H., and Richter, C. P., Use of electrical skin resistance method in the study of patients with neck and upper extremity pain. Johns Hopkins Med. J. 137:69–74, 1975.
Liszka-Hackzell, J. J., and Martin, D. P., Categorization and analysis of pain and activity in patients with low back pain using a neural network technique. J. Med. Syst. 26(4):337–347, 2002.
Shankar, K., Bharathi, V. S., and Daniel, J., An empirical approach for objective pain measurement using dermal and cardiac parameters. ICBME 2008(23):678–681, 2009.
Weng, C. S., Tsai, Y. S., Shu, S. H., Chen, C. C., and Sun, M. F., The treatment of upper back pain by two modulated frequency modes of acupuncture-like tens. J. Med. Biol. Eng. 25(1):21–25, 2005.
Weng, C. S., Tsai, Y. S., and Yang, C. Y., Using electrical conductance as the evaluation parameter of pain in patients with low back pain treated by acupuncture like tens. Biomed. Eng. Appl. Basis Commun. 16:205–212, 2004.
Bounds, D. G., Lloyd, P. J., Mathew, B., and Waddell, G., A multi layer perceptron network for the diagnosis of low back pain. IEEE Int. Conf. Neural Netw. 2:481–489, 1988.
Lin, L., Hu, P. J. H., and Sheng, O. R. L., A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations. Decis. Support Syst. 42:1152–1169, 2006.
Gioftsos, G., and Grieve, D. W., The use of artificial neural networks to identify patients with chronic low-back pain conditions from patterns of sit-to-stand manoeuvres. Clin. Biomech. 11(5):275–280, 1996.
Vaughn, M. L., Cavill, S. J., Taylor, S. J., Foy, M. A., and Fogg, A. J. B. Direct explanations and knowledge extraction from a multilayer perceptron network that performs low back pain classification. In: Wermter, S., and Sun, R. (Eds.), Hybrid Neural Systems. Lecture Notes in Artificial Intelligence. Springer, 2000.
Mathew, B., Morris, D., David, H., and Gordon, W., Artificial intelligence in the diagnosis of low-back pain and sciatica. Spine 13(2):168–172, 1988.
Li, B., Yan, C., and Xu, Y. Designing and implementing of an expert system for the differential diagnosis and treatment of lumbago. Proceedings of the 3rd International Conference on Young Computer Scientists. Beijing, 1027–1028, 1993.
Forsstrom, J. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27:509–517, 1995.
Carregal, A., Figueira, A., Núñez, M., Carollo, A., Lorenzo, A., Rey, M., and González, G., Fuzzy logic and postoperative pain. Rev. Esp. Anestesiol. Reanim. 44(6):215–217, 1997.
Shieh, J. S., Dai, C. Y., Wen, Y. R., and Sun, W. Z., A novel fuzzy pain demand index derived from patient-controlled analgesia for postoperative pain. IEEE Trans. Biomed. Eng. 54(12):2123–2132, 2007.
Shamim, M. S., Enam, S. A., and Qidwai, U., Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg. Neurol. 72(6):565–572, 2009.
Acknowledgement
For their generous contribution to the conducted study, the employees at the Dumlupinar University, Medical Faculty, Department of Physical Therapy and Rehabilitation outpatient clinic are appreciated.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sari, M., Gulbandilar, E. & Cimbiz, A. Prediction of Low Back Pain with Two Expert Systems. J Med Syst 36, 1523–1527 (2012). https://doi.org/10.1007/s10916-010-9613-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-010-9613-x