A Fuzzy Expert System for Diagnosis of Epilepsy Diseases Using the Situational Logic and ACH Modeling in the Creation of Knowledge Base

Document Type : Research Paper

Authors

1 Ph.D in Computer Engineering and Information System, Payame Noor University, Tehran, Iran

2 Department of computer, faculty of electric and computer engineering, Azad Islam University Mahshahr branch, Mahshahr, Iran

Abstract

Nowadays expert systems are used as one of the most useful and most practical decision support systems. These systems are relying on knowledge of experts in certain domain combines valuable experience with speed and accuracy of computer and improve the quality of their judgments. One of the most extensive applications of these systems is medical diagnostic fields. Different from what happened in most prior researches in the development of expert systems, in the present study using the situational logic in the process of knowledge acquisition and fuzzy inference engine architecture approach is recommended. Identify the type of epilepsy has always been one of the most challenging deliberations among the neurologist doctors and strict distinction between the types of the disease, according to the closely signs Creates conflict among the field's doctors that the expert system is able to solve this problem with the accuracy of 83 percent. Forming a comprehensive knowledge base using analysis of competing hypotheses (ACH) modeling in order to distinguish between 14 types of epilepsy disease is the distinctive features of this study. Research done on the project can be used to diagnose other diseases that have similar symptoms closely and to be pragmatic. The proposed system can be used in situations where access to neurologist doctors is impossible can be very useful.

Keywords

References

Amooji, A.(2015). Analytical comparsion of methematical modelling in the diagnostic expert systems. International Journal of Computer Applications Technology and Research, 12(4), 933-935.
Breuker, J., Van de Velde, W. (2006). The Common KADS library for expertise modeling. Amesterdam: IOS press.
Buyuk, B., Sisman, A., Akyildiz, M. & Alparslan, F. (2007). Adaptive neuro-fuzzy inference system (anfis). A new approach to predictive modeling in applications: A study of neuro-fuzzy modeling of pcp-based receptor antagonists. Bioorganic & Medicinal Chemistry,, 12(15), 4265-4282.
Endsley, R., Farley, C., Jones, M., Midkiff, H., Hansman, J. (1998). Situation awareness information requirements for commercial airline pilots. Cambridge: Massachusetts Institute of Technology, International Center for Air Transportation.
Endsley, R. (1995). A taxonomy of situation awareness errors. England: Avebury Aviation & Ashgate Publishing Ltd.
Frederick, H. (2005). Rule-based Systems Sulotions. Communication of the ACM, 9(28), 921-932.
Golabchi, M. & Faraji, A. (2015). Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects. Journal of Industrial Management, 7(4), 837-860. (in Persian)
Hayek, P. (2010). Fuzzy logic. Stanford: Stanford Encyclopedia of Philosophy.
Heuer, J., Richards, J. (1999). Psychology of intelligence analysis, Center for Study of Intelligence. USA:Central Intelligence Agency.
Jampour, M., Jampour, M., Ashourzadeh, M,. Yaghubi, M. (2011). A Fuzzy Expert System to Diagnose Diseases with Neurological Signs in Domestic Animal. Information Technology: New Generations (ITNG), Eighth International Conference on – IEEE:351-357.
Johen, Y. (2006). A Reasoning Based on an Extended Dempester-Shifer Theory. AAAI, 10(86), 125-133.
Karimov, S., Rahimova, N. (2004). Expert systems. Baku: Chashioghlu.
Khorramian Toosi, S., Zeinali, B. (2014). Designing a decision-making system for the treatment of dental caries in children. Journal of Development Strategies in Medical Education, 1(1), 37-44. (in Persian)
Kleer, J. (2006). An Assumption-based Management System. Artif Intell, 2(28), 127-162.
Langarizadeh, M., Khajepour, E., Khajepour, H. Noori, T. (2014). Fuzzy Expert System for the diagnosis of bacterial meningitis from other meningitis in children. Journal of Health and Biomedical Informatics Medical Informatics Research Center, 1(1), 19-25. (in Persian)  
Manuel, M., Tavares, A., Simons, R. (2008). Development in E-health and Telemedicine. London: PLT pub.
Martin, L. )2001(. Knowledge Acquisition and Evaluation of Expert System for Managing Disorders of Outer Eye. Computers in Nursing, 19(3), 114-127.
Moad, J. (2006). Object Methods Tame Reengineering Madness. Data mation, (15), 43-48.
Mohammadi Motlagh, H., Mohammadi Motlagh, A., Rezaee Noor, J. (2015). Design an expert system for evaluation and selection supplier. Journal of Industrial Management, 7(2), 385-403. (in Persian)
Nawell, A. (1996). Production system: models of control structures. Visual information processing. Academic Press, 2(12), 493-525.
Patrick, H. (2008). Artificial Intelligence. Boston: Addison-Wesley.

Rajdeep, B., Sugata, S. (2012). Rule Based Expert System for Diagnosis of Neuromuscular Disorders. International Journal of Advanced Networking Applications, 2012(4), 2699-2710.

Reggia, J. (1995). Adjuctive inference in Proc of Expert System in Government Symposium. IEEE Press, 4(27), 484-489.
Robinson, J. (2007). A Machine-Oriented Logic Based on the Resolution Principle. Journal of the ACM, 1(12), 23-41.
Roger, C., Abelson, R. (2007). Scripts, Plans, Goals and understanding. New Jersey: Lawrence Elbaum.
Sadeghi Moghaddam, M., Safari, H., AhmadiNozari, M. M. (2015). Measuring sustainability of service supply chain by using a multi-stage/multicast fuzzy inference system (Studied Case: Parsian Bank). Journal of Industrial Management, 7(3), 533-562. (in Persian)
 Sohrabi, B., Tahmasebipur, K., Raeesi Vanani, I . (2011). Designing a Fuzzy Expert System for ERP Selection. Journal of Industrial Management, 6(3), 39-58. (in Persian)
Stefan, M. (1993). Introduction to Knowledge Systems. London: Los Altos.
Toloie Eshlaghi, A., Mohsen Taheri, S. (2010). Designing an Expert System for Suggesting the Blood Cancer Treatment. Health Management, 13(40), 41-51. (in Persian)
Xiaodong S., Hao Y., Feng L., Mac A. (2005). A fuzzy discrete event system for hiv/aids treatment. The 14th IEEE International Conference on Fuzzy Systems. Reno, Nevada, May, 167-172.
Zadeh, A. (1994). Review of book A Mathematical theory of Evidence. AI Magazine, 12(4), 81-83.
 
Industrial Management Journal
Volume 9, Issue 2
2017
Pages 353-382

Files

Share

How to cite

Statistics