Abstract
Low back pain (LBP) appears to be the foremost contributor to the years lived with disability globally, depriving many individuals across the nations of leading daily activities. Diagnosing LBP is quite challenging as it requires dealing with several clinical variables having no precisely quantified values. With the goal to design a reliable medical expert system for assessment and management of LBP, the research offers a lattice-based scheme for efficient representation of relevant medical knowledge, proposes a suitable methodology for design of a fuzzy knowledge base that can handle imprecision in knowledge, and derives a fuzzy inference system. A modular approach is taken to construct the fuzzy knowledge base, where each module is able to capture interrelated clinical knowledge about medical history, findings of physical examinations, and pathological investigation reports. The fuzzy inference system is designed based on the Mamdani method. For fuzzification, the design adopts triangular membership function; for defuzzification, the centroid of area technique is used. With the relevant medical knowledge being acquired from the expert physicians, a working prototype of the system has been built. The prototype has been successfully tested with some LBP patient records available at the ESI Hospital, Sealdah. The designed prototype is found to be clinically acceptable among the expert and non-expert physicians.
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References
Liao, S. H. (2005). Expert system methodologies and applications—A decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103.
Andersson, G. B. (1999). Epidemiological features of chronic low-back pain. The Lancet, 354(9178), 581–585.
Shortliffe, E. (Ed.). (2012). Computer-based medical consultations: MYCIN (Vol. 2). Elsevier.
Kulikowski, C. A., & Weiss, S. M. (1982). Representation of expert knowledge for consultation: The CASNET and EXPERT projects. Artificial Intelligence in Medicine, 51, 21–55.
Miller, R. A., Pople, H. E., Jr., & Myers, J. D. (1982). Internist-I, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine, 307(8), 468–476.
Shortliffe, E. H., Scott, A. C., Bischoff, M. B., Campbell, A. B., Van Melle, W., & Jacobs, C. D. (1984). An expert system for oncology protocol management. In Buchanan, B. G. and Shortiffe, E. H. (Eds.), Rule-based expert systems (pp. 653–665).
Aikins, J. S., Kunz, J. C., Shortliffe, E. H., & Fallat, R. J. (1983). PUFF: An expert system for interpretation of pulmonary function data. Computers and Biomedical Research, 16(3), 199–208.
Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics and Systems, 29(7), 661–688.
Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Upper Saddle River (p. 563).
Adeli, A., & Neshat, M. (2010). A fuzzy expert system for heart disease diagnosis. In Proceedings of international multi conference of engineers and computer scientists, Hong Kong (Vol. 1).
Samuel, O. W., Omisore, M. O., & Ojokoh, B. A. (2013). A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever. Expert Systems with Applications, 40(10), 4164–4171.
Fathi-Torbaghan, M., & Meyer, D. (1994). MEDUSA: A fuzzy expert system for medical diagnosis of acute abdominal pain. Methods of Information in Medicine, 33(5), 522–529.
Kadhim, M. A., Alam, M. A., & Kaur, H. (2011). Design and implementation of fuzzy expert system for back pain diagnosis. International Journal of Innovative Technology & Creative Engineering, 1(9), 16–22.
Allegri, M., Montella, S., Salici, F., Valente, A., Marchesini, M., Compagnone, C., and Fanelli, G. (2016). Mechanisms of low back pain: A guide for diagnosis and therapy. F1000Research, 5.
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The authors are thankful to the ESI Hospital Sealdah, Kolkata, India, for providing access to LBP patient records for fulfilment of this research work.
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Santra, D., Basu, S.K., Mandal, J.K., Goswami, S. (2020). Lattice-Based Fuzzy Medical Expert System for Management of Low Back Pain: A Preliminary Design. In: Mandal, J., Banerjee, S. (eds) Intelligent Computing: Image Processing Based Applications. Advances in Intelligent Systems and Computing, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-4288-6_8
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DOI: https://doi.org/10.1007/978-981-15-4288-6_8
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