Type-2 fuzzy rule-based expert system for diagnosis of spinal cord disorders

Document Type : Article

Authors

1 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

2 -. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran. - Knowledge Intelligent System Laboratory, University of Toronto, Toronto, Canada.

3 - Knowledge Intelligent System Laboratory, University of Toronto, Toronto, Canada. - Department of Industrial Engineering, TOBB University of Economics and Technology, Ankara, Turkey

4 Fayyazbakhsh and Erfan Hospitals, Tehran, Iran

Abstract

The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Using of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians’ diagnosis. The outputs of the system are comprised of type of disorder, location and severity as well as the necessity of taking a M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

Keywords

Main Subjects


  1. Andersson, G.B. Epidemiological features of chronic low-back pain", The Lancet, 354(9178), pp. 581-585 (1999). 2. Koh, J., Chaudhary, V., Dhillon, G. Disc herniation diagnosis in MRI using a CAD framework and a twolevel classi_er", Int J Comput Assist Radiol Surg, 7(6), pp. 861-869 (2012). 3. Patel, V.L., Shortli_e, E.H., Stefanelli, M., Szolovits, P., Berthold, M.R., Bellazzi, R., and Abu-Hanna, A. The coming of age of arti_cial intelligence in medicine", Arti_cial Intelligence in Medicine, 46(1), pp. 5-17 (2009). 4. Miller, P.L. The evaluation of arti_cial intelligence systems in medicine", Comput Methods Programs Biomed, 22(1), pp. 5-11 (1986). 5. Seising, R. From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis", Artif Intell Med, 38(3), pp. 237-256 (2006). 6. Bharti, P.K., Silawat, N., Singh, P.P., Singh, M.P., Shukla, M., Chand G., and Singh, N. The usefulness of a new rapid diagnostic test, the _rst response malaria combo (pLDH/HRP2) card test, for malaria diagnosis in the forested belt of central India", Malar J, 7, p.126 (2008). 7. Obot, O.U. and Uzoka, F.M. Experimental study of fuzzy-rule based management of tropical diseases: case of malaria diagnosis", Stud Health Technol Inform, 137(1), pp. 328-339 (2008). 8. Akinyokun, C.O., Obot, O.U., Uzoka, F.M., and Andy, J.J. A neuro-fuzzy decision support system for the 470 S. Rahimi Damirchi-Darasi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 455{471 diagnosis of heart failure", Stud Health Technol Inform, 156(1), pp. 231-244 (2010). 9. Fazel Zaranid, M.H., Zolnoori, M., Moin, M., and Heidarnejad, H. Fuzzy rule-based expert system for diagnosing asthma", Scientia Iranica, 17(2), pp. 129- 142 (2010). 10. Kadhim, M.A., Alam, M.A., and Kaur, H. Design and implementation of fuzzy expert system for back pain diagnosis", Int Journal of Innovative Technology & Creative Engineering, 1(9), pp. 16-22 (2011). 11. Sari, M., Gulbandilar, E., and Cimbiz, A. Prediction of low back pain with two expert systems", J Med Sys, 36(3), pp. 1523-1527 (2012). 12. Esteban, B., Tejeda-Lorente, A., Porcel, C., Arroyo, M., and Herrera-Viedma, E. TPLUFIB-WEB: A fuzzy linguistic Web system to help in the treatment of low back pain problems", Knowledge-Based Systems, 67(1), pp. 429-438 (2014). 13. Gulbandialr, E., Sari, M., and Cimbiz, A. Prediction of low back pain using a fuzzy logic algorithm", American Journal of Biomedical Science and Engineering, 1(5), pp. 58-62 (2015). 14. Ohri, K., Singh, H., and Sharma, A. Fuzzy expert system for diagnosis of breast cancer", Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on, Chennai, India, pp. 2487-2492 (2016). 15. Gal, N., Andrei D., Stoicu-Tivadar V., Neme_s D.I., and N_nd_a_san E. Fuzzy expert system prediction of lumbar spine subchondral sclerosis and lumbar disk hernia", Soft Computing Applications, pp. 2487-2492, Springer, Cham (2016). 16. Katigari, M.R., Ayatollahi, H., Malek, M., and Haghighi, M.K. Fuzzy expert system for diagnosing diabetic neuropathy", World Journal of Diabetes, 8(2), p. 80 (2017). 17. Fazel Zarandi, M.H., Zarinbal, M., and Izadi, M. Systematic image processing for diagnosing brain tumors: A type-II fuzzy expert system approach", Applied Soft Computing, 11(1), pp. 285-294 (2011). 18. Rahimi Damirchi-Darasi, S., Fazel Zarandi, M.H., and Izadi, M. Type-2 fuzzy hybrid expert system for diagnosis of degenerative disc diseases", Modeling, Identi_cation, Simulation & Control (AIJ-MISC), 45(2), pp. 53-62 (2013). 19. Zarinbal, M., Turksen, L.B., Fazel Zarandi, M.H., and Izadi, M. Interval type-2 fuzzy image processing expert system for diagnosing brain tumors", Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on, Boston, MA, USA, pp. 1-8 (2014). 20. Zarinbal, M., Fazel Zarandi, M.H., Turksen, L.B., and Izadi, M. A type-2 fuzzy image processing expert system for diagnosing brain tumors", Journal of Medical Systems, 39(10), p. 1 (2015). 21. Fazel Zarandi, M.H., Rahimi Damirchi-Darasi, S., Izadi, M., Turksen, I.B., and Arabzadeh Ghahazi, M. Fuzzy rule based expert system to diagnose spinal cord disorders", Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on, Boston, MA, USA, pp. 1-5 (2014). 22. Mechanical pain de_nition", Retrieved 1/15/2016, from: http://www.spine-health.com/glossary/mechanicalpain 23. Diseases and conditions herniated disk", Retrieved 1/15/2016, from http://www.mayoclinic.org/diseasesconditions/ herniated-disk/basics/de_nition/c on-20029957 (2014). 24. Diseases and conditions spinal stenosis", Retrieved 1/15/2016, from http://www.mayoclinic.org/diseasesconditions/ spinal-stenosis/basics/de_nition/ con-20036105. 25. Revord, J.P. Typical symptoms of a herniated disc", Retrieved 1/15/2016, from http://www.spinehealth. com/conditions/herniated-disc/typicalsymptoms- a-herniated-disc. 26. Staehler, R.A. Cervical herniated disc symptoms and treatment options", Retrieved 1/15/2016, from http://www.spine-health.com/conditions/herniateddisc/ cervical-herniated-disc-symptoms-andtreatment- options 27. Marieb, E.N. and Hoehn, K., Human Anatomy & Physiology, Pearson Education (2007). 28. Yellow ags in back pain", Retrieved 1/15/2016, from: http://www.she_eldbackpain.com/professionalresources/ learning/in-detail/yellow-ags-in-back-pain. 29. Mendel, J.M. and John, R.I.B. Type-2 fuzzy sets made simple", Fuzzy Systems, IEEE Transactions on, 10(2), pp. 117-127 (2002). 30. Mendel, J.M. Advances in type-2 fuzzy sets and systems", Information Sciences, 177(1), pp. 84-110 (2007). 31. Emami, M.R., Turksen, I.B., and Goldenberg, A.A. A uni_ed parameterized formulation of reasoning in fuzzy modeling and control", Fuzzy Sets and Systems, 108(1), pp. 59-81 (1999). 32. Mendel, J., Hagras, H., Tan, W.-W., Melek, W.W., and Ying, H., Introduction to Type-2 Fuzzy Logic Control: Theory and Applications, John Wiley & Sons (2014). 33. Karnik, N.N. and Mendel, J.M. Introduction to type- 2 fuzzy logic systems", IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228), Anchorage, AK, pp. 915-920 (1998). 34. Karnik, N.N., Mendel, J.M., and Qilian, L. Type-2 fuzzy logic systems", Fuzzy Systems, IEEE Transactions on, 7(6), pp. 643-658 (1999). 35. Leekwijck, W.V. and Kerre, E.E. Defuzzi_cation: criteria and classi_cation", Fuzzy Sets and Systems, 108(2), pp. 159-78 (1999). S. Rahimi Damirchi-Darasi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 455{471 471 36. Spinal disc problems (including red ag signs)", Retrieved 1/15/2016, from http://patient. info/doctor/ spinal-disc-problems-including-red-ag-signs. 37. Hochschuler, S.H. Back pain risk factors: What can increase the potential for back problems?" Retrieved 1/15/2016, from http://www.spine-health. com/conditions/lower-back-pain/back-pain-riskfactorswhat- can-increase-potential-back-problems 38. Liang, Q. and Mendel, J.M. Interval type-2 fuzzy logic systems: theory and design", Fuzzy Systems, IEEE Transactions on, 8(5), pp. 535-550 (2000)