Fuzzy Expert System Generalised Model for Medical Applications

Abstract

Over the past two decades an exponential growth of medical fuzzy expert systems has been observed. These systems address specific forms of medical and health problems resulting in differentiated models which are application dependent and may lack adaptability. This research proposes a generalized model encompassing major features in specialized existing fuzzy systems. Generalization modelling by design in which the major components of differentiated the system were identified and used as the components of the general model. The prototype shows that the proposed model allows medical experts to define fuzzy variables (rules base) for any medical application and users to enter symptoms (facts base) and ask their medical conditions from the designed generalised core inference engine. Further research may include adding more composition conditions, more combining techniques and more tests in several environments in order to check its precision, sensitivity and specificity.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] F. Bobillo, M. Delgado, J. Gómez-Romero and U. Straccia, “Fuzzy Description Logics Under Gödel Semantics,”International Journal of Approximate Reasoning, vol. 50, no. 3, pp. 494–514, Mar. 2009. https://doi.org/10.1016/j.ijar.2008.10.003

  • [2] D. E. Heckerman and E. H. Shortliffe, “From Certainty Factors to Belief Networks,” Artificial Intelligence in Medicine, vol. 4, no. 1, pp. 35–52, Feb. 1992. https://doi.org/10.1016/0933-3657(92)90036-O

  • [3] B. O. Emokhare and E. M. Igbape, “Fuzzy Logic Based Approach to Early Diagnosis of Ebola Hemorrhagic Fever,” in Proceedings of the World Congress on Engineering and Computer Science WCECS 2015, San Francisco, USA, October 21–23, 2015.

  • [4] T. Ross, Fuzzy Logic With Engineering Applications. New Mexico: Wiley, 2017.

  • [5] C. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 2, Mar./Apr. 1990. https://doi.org/10.1109/21.52552

  • [6] K. Tanaka, An Introduction to Fuzzy Logic for Practical Applications. Springer, 1997.

  • [7] TutorialsPoint, “Fuzzy Logic – Applications,” TutorialsPoint, 2019.

  • [8] R. R. Yager andL.A. Zadeh, Eds., An Introduction to Fuzzy Logic Applications in Intelligent Systems. New York: Springer Science + Business Media LLC, 1992. https://doi.org/10.1007/978-1-4615-3640-6

  • [9] H. Singh, M. M. Gupta, T. Meitzler, Z.-G. Hou, K. K. Garg, A. M. G. Solo, and L. A. Zadeh, “Real-Life Applications of Fuzzy Logic,” Advances in Fuzzy Systems, pp. 1–3, 2013. https://doi.org/10.1155/2013/581879

  • [10] P. Larsen, “Industrial Applications of Fuzzy Logic Control,” International Journal of Man-Machine Studies, vol. 12, iss. 1, pp. 3–10, 1980. https://doi.org/10.1016/S0020-7373(80)80050-2

  • [11] B. Singh and A. K. Mishra, “Fuzzy Logic Control System and Its Applications,” International Research Journal of Engineering and Technology (IRJET), vol. 2, no. 8, pp. 742–746, 2015.

  • [12] S. S. Smita, S. Sushil, and M. S. Ali, “Design of Fuzzy Expert Systems for Diagnosis of Cardiac Diseases,”International Journal of Medical Science and Public Health, vol. 2, no. 1, pp. 56–61, 2013.

  • [13] S. Mishraand and M. Prakash, “Study of Fuzzy Logic in Medical Data Analytics,” International Journal of Pure and Applied Mathematics, vol. 119, no. 12, pp. 16321–16342, 2018.

  • [14] A. Patel, S. K. Gupta, Q. Rehman, and M. K. Verma, “Application of Fuzzy Logic in Biomedical Informatics,”Journal of Emerging Trends in Computing and Information Sciences, vol. 4, no. 1, pp. 57–62, 2013.

  • [15] H. Ahmadi, M. Gholamzadeh, L. Shahmoradi, M. Nilashi, and P. Rashvand, “Diseases Diagnosis Using Fuzzy Logic Methods: A Systematic and Meta-Analysis Review,” Computer Methods and Programs in Biomedicine, vol. 161, Jul. 2018. https://doi.org/10.1016/j.cmpb.2018.04.013

  • [16] M. A. M. Reis, N. R. S. Ortega, and P. S. P. Silveira, “Fuzzy Expert System in the Prediction of Neonatal Resuscitation,” Brazilian Journal of Medical and Biological Research, vol. 37, no. 5, pp. 755–764, May 2004. https://doi.org/10.1590/S0100-879X2004000500018

  • [17] P. Nath, “AI & Expert System in Medical Field: A Study by Survey Method,” AITHUN, vol. 1, Mar. 2015.

  • [18] D. G. Bobrow, Ed., Artificial Intelligence in Perspective. London: MIT/Elsevier, 1994. https://doi.org/10.7551/mitpress/1413.001.0001

  • [19] A. K. Meena and S. Kumar, “Study and Analysis of MYCIN Expert System,” International Journal Of Engineering And Computer Science, vol. 4, no. 10, pp. 14861–14865, Oct. 2015.

  • [20] B. G. Buchanan and E. H. Shortliffe, Eds., Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison Wesley, 1984.

  • [21] J. Liebowitz, “Worldwide Perspectives and Trends in Expert Systems. An Analysis Based on the Three World Congresses on Expert Systems,” AI Magazine, vol. 18, no. 2, 1997.

  • [22] B. S. Tchindaa, M. Noubomb, D. Tchiotsopa, V. Louis-Dorrc, and D. Wolfc, “ Towards an Automated Medical Diagnosis System for Intestinal Parasitosis,” Informatics in Medicine Unlocked, vol. 13, pp. 101–111, 2018. https://doi.org/10.1016/j.imu.2018.09.004

  • [23] K. S. Metaxiotis and J. E. Samouilidis, “Expert Systems in Medicine: Academic Illusion or Real Power?,” Information Management & Computer Security, vol. 8, no. 2, pp. 75–79, May 2000. https://doi.org/10.1108/09685220010694017

  • [24] M. S. Olivier, Information Technology Research – A Practical Guide for Computer Science and Informatics. Van Schaik, 2009.

  • [25] V. Atanassova and S. Sotirov, “A new formula for de-i-fuzzification of intuitionistic fuzzy sets,” Notes on Intuitionistic Fuzzy Sets, vol. 18, no. 3, 2012, pp. 49–51, September 2012.

  • [26] S. Kumar and G. Kaur, “Detection of Heart Diseases Using Fuzzy Logic,” International Journal of Engineering Trends and Technology (IJETT), vol. 4, no. 6, pp. 2694–2699, Jun. 2013.

  • [27] K. K. Oad and X. DeZhi, “A Fuzzy Rule Based Approach to Predict Risk Level of Heart,” Global Journal of Computer Science and Technology (C) Software & Data Engineering, vol. 14, no. 3, 2014.

  • [28] O. Oluwagbemi, F. Oluwagbemi, and O. Abimbola, “Ebola Fuzzy Informatics Systems on the Diagnosis, Prediction and Recommendation of Appropriate Treatments for Ebola Virus Disease (EVD),” Informatics in Medicine Unlocked, vol. 2, pp. 12–37, 2016. https://doi.org/10.1016/j.imu.2015.12.001

  • [29] M. Hussain, Fuzzy Relations, Blekinge Institute of Technology, Blekinge, May 2010.

  • [30] M. Siddique, Fuzzy Decision Making Using Max-Min Method and Minimization of Regret Method(MMR), Blekinge Institute of Technology, Blekinge, Jun. 2009.

OPEN ACCESS

Journal + Issues

Search