Besime Erin
ABSTRACT
High level of expertise is required for human disease diagnosis which is a complicated and difficult process. Each disease is characterised with the set of observable sign and symptoms. Based on these symptoms to understand patient health problems and to make a diagnosis of these diseases with their clear definition is difficult. The diagnosis of the disease is based on knowledge of doctor physicians. Fuzzy logic is one of the best approaches to design knowledge-based system for diagnosis of the diseases. In this paper, the design of a type-2 fuzzy system is performed for diagnosis of the common diseases using proper values of the inputs. The input symptoms and output diseases are defined for construction of the fuzzy rule base. The relationships are presented using type-2 IF-Then rules. Based on the fuzzy rules the design of type-2 fuzzy inference system is performed. The designed system will help the physician to diagnose common diseases such as common cold and flu.
- Easydiagnosis, an online diagnosis tool. Available: http://www.yourdiagnosis.comGoogle Scholar
- Wrongdiagnosis: Symptoms of different diseases. www.wrongdiagnosis.comGoogle Scholar
- WebMD: symptoms of different diseases.www.webmd.comGoogle Scholar
- Zadeh, L. A. 1965. Fuzzy sets, Information and Control, 8, 338--353Google ScholarCross Ref
- Zadeh LA. 1975. The Concept of Linguistic Variable and its Application to Approximate Reasoning, Information Sciences, 8, 199--249Google ScholarCross Ref
- Awotunde J.B., Matiluko O.E. and Fatai O.W.2014. Medical Diagnosis System Using Fuzzy Logic, African Journal of Computing & ICT, 7(2), 99--106Google Scholar
- Sikchi S.S., Sikchi S., Ali M. S. 2013. Fuzzy Expert System (FES) Medical Diagnosis, International Journal of Computer Application, 63, 11, 7--16Google ScholarCross Ref
- Hasan M.A,. Sher-E-Alam K.M., Chowdhury A.R. 2010. Human Disease Diagnosis Using Fuzzy Expert System?, Journal of Computing, 2, 6, 66--70Google Scholar
- Dagar P., Jatain A., Gaur D. 2015. Medical Diagnosis Using Fuzzy Logic Toolbox, In Proceed. Of the IEEE International Conference on Computing, Communicating, Communication and Automation, 193--197Google ScholarCross Ref
- Rana M., Sedamkar R.R. 2013. Design of Expert System for Medical e Diagnosis Using Fuzzy Logic, Inter. Journal of Scientific & Engineering Research, 4, 6, 2914--2921Google Scholar
- Mishra N., Jha P. 2014. A Review on the Application of Fuzzy Expert System for Disease Diagnosis, International Journal of Advance Research in Engineering and Applied Science, 3, 12, 28--43Google Scholar
- Sikchi S.S., Sikchi S. 2016. Fuzzy Expert System for Medical Diagnosis?, International Journal of Innovative and Emerging Research in Engineering, 3, 1, 91--96.Google Scholar
- Pabbi V. 2016. Fuzzy Expert System for Medical Diagnosis, International Journal of Scientific and Research Publication, 5, 1, 91--96.Google Scholar
- Arya C., Tiwari R. 2016. Expert System for Breast Cancer Diagnosis: A Survey, In Proceed. Of the IEEE Inter. Conf. on Computer Communication and Informatics, 1--9.Google ScholarCross Ref
- Abiyev RH, Abizade S. 2016. Diagnosing Parkinson's Diseases Using Fuzzy Neural System. Computational and Mathematical Methods in Medicine. 2016(3): 1--9.Google ScholarCross Ref
- Abiyev RH. 2009. Fuzzy Wavelet Neural Network for Prediction of Electricity Consumption. AIEDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 23(2), 109--118. Google ScholarDigital Library
- Mendel JM. 2001. Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions. Prentice Hall, Upper Saddle River, NJ.Google Scholar
- Karnik NN, Mendel JM, Liang Q. 1999. Type-2 Fuzzy Logic Systems. IEEE Trans. Fuzzy System, 7, 643--658. Google ScholarDigital Library
- Mendel JM, John RI, Liu F. 2006. Interval Type-2 Fuzzy Logic Systems Made Simple, IEEE Trans. Fuzzy Systems, 14, 6, 808--821. Google ScholarDigital Library
- Hagras H. 2004. A Hierarchical Type-2 Fuzzy Logic Control Architecture For Autonomous Mobile Robots. IEEE Trans. on Fuzzy System, 12, 4, 524--539. Google ScholarDigital Library
- Abiyev RH. 2014. Credit Rating using Type-2 Fuzzy Neural Networks. Mathematical Problems in Engineering, 2014.Google ScholarCross Ref
- Abiyev R.H., Erin B., Denker A. 2017. Navigation of Mobile Robot Using Type-2 Fuzzy System. In Proceed. of the International Conference on Intelligent Computing (Liverpool, UK, August 7--10), Lecture Notes in Artificial Intelligence (LNAI), Springer, 608--616Google Scholar
- Abiyev R.H., Uyar K., Ilhan U., Imanov E. 2016. Assessment of Food Security Risk Level Using Type 2 Fuzzy System. In Proceed. of the 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016 Book Series: Procedia Computer Science, 102, 547--554. Google ScholarDigital Library
- Abiyev RH, Kaynak O, Kayacan E. 2013. A type-2 fuzzy wavelet neural network for system identification and control. Journal of the Franklin Institute-Engineering and Applied Mathematics. 350, 7, 1658--1685.Google ScholarCross Ref
- Abiyev RH, Kaynak O, Alshanableh T, Mamedov F. 2011. A Type-2 Neuro-fuzzy System Based on Clustering and Gradient Techniques Applied to System Identification and Channel Equalization. Applied Soft Computing. 11, 1, 1396--1406. Google ScholarDigital Library
- Abiyev R.H, Kaynak O. 2010. Type-2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants. IEEE Transactions on Industrial Electronics. 57, 12, 4147--4159.Google ScholarCross Ref
- Abiyev R.H. 2010. Type-2 Fuzzy Wavelet Neural Network for Time-Series Prediction. (IEA-AIE 2010), Trends in Applied Intelligent Systems, Part III, Book Series: Lecture Notes in Artificial Intelligence, 6098, 518--527 Google ScholarDigital Library
- Abiyev R.H., Akkaya N., Gunsel I. 2018. Control of Omnidirectional Robot Using Z-Number-Based Fuzzy System, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, Issue 1, 2019, pp.238--252.Google Scholar
Index Terms
- Diagnosis of Common Diseases Using Type-2 Fuzzy System
Recommendations
Overview of Type-2 Fuzzy Logic Systems
Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 ...
A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system
As the analysis and diagnosis of gynecological diseases, especially using the homoeopathic system of medicine, gets more and more complicated, it becomes important for us to develop a decision-support system which can help a gynecologist analyze and ...
Modeling uncertainty in clinical diagnosis using fuzzy logic
This paper describes a fuzzy approach to computer-aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps. A constraint satisfaction ...
Comments