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An ANFIS perspective for the diagnosis of type II diabetes

Murat Kirisci|Hasan Yilmaz|M. Ubeydullah Saka
기초자연과학연구소 (The Natural Science Institute Wonkwang University)|2019.04|ANNALS OF FUZZY MATHEMATICS AND INFORMATICS|제17권 제2호|pp. 101-113|DOI 10.30948/afmi.2019.17.2.101

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키워드

MedicinePCAANFISType 2 diabetes mellitusSoft computing

초록

In this paper, we have detected on diabetes disease, which is a very common and important disease using Principal Component Analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. Firstly, we will introduce soft computing(SC) methods and briefly refer to the applications of these methods in medicine. After, an intelligent diagnostic system for diabetes will then be developed on the PCA and the ANFIS. The structure of ANFIS with PCA intelligent system for diagnosis of diabetes is composed by two processes: In the first process, it is used PCA to find that the distinct classes of T2DM subjects and controls. Second, features of two classes(healthy and T2DM patients) obtained in first process are given to inputs of ANFIS classifier.

Abstract

In this paper, we have detected on diabetes disease, which is a very common and important disease using Principal Component Analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. Firstly, we will introduce soft computing(SC) methods and briefly refer to the applications of these methods in medicine. After, an intelligent diagnostic system for diabetes will then be developed on the PCA and the ANFIS. The structure of ANFIS with PCA intelligent system for diagnosis of diabetes is composed by two processes: In the first process, it is used PCA to find that the distinct classes of T2DM subjects and controls. Second, features of two classes(healthy and T2DM patients) obtained in first process are given to inputs of ANFIS classifier.

참고문헌 (41)

  1. M. F. Abbod, "Survey of utilisation of fuzzy technology in Medicine and Healthcare", Fuzzy Sets and Systems, 120, pp.331~349 (2001)
  2. K. P. Adlassnig, "A fuzzy logical model of computer-assisted medical diagnosis", Methods Inf. Med, 19(3), pp.141~148 (1980)
  3. H. Axer, "The application of fuzzy-based methods to central nerve fiber imaging", Artif. Intell. Med, 29(3), pp.225~239 (2003)
  4. S. Barro, "Fuzzy Logic in Medicine" (2002), Physica
  5. I. A. Basheer, "Artificial neural networks: fundamentals, computing, design, and Application", J. Microbiol. Methods, 43, pp.3~31 (2000)
  6. W. G. Baxt, "Application of artificial neural Networks to clinical medicine", Lancet, 346, pp.1135~1138 (1995)
  7. K. Boegl, "Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system", Arti cial Intelligence in Medicine, 30(1), pp.1~26 (2004)
  8. M. E. Brandt, "Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images", Comput. Med. Imag. Graph, 18(1), pp.25~34 (1994)
  9. H. B. Burke, "Artificial Neural Networks Improve the Accuracy of Cancer Survival Prediction", CANCER, 79(4), pp.857~862 (1997)
  10. D. Gardner, "Greenspan's Basic and Clinical Endocrinology 9th ed" (2011), McGraw- Hill
  11. M. T. Hagan, "Neural Network Design, 2nd ed" (1996), PWS Publishing
  12. A. E. Hassanien, "Intelligent data analysis of breast cancer based on rough set theory", Interna- tional Journal on Artificial Intelligence Tools, 12(4), pp.465~479 (2003)
  13. S. Haykin, "Neural Networks - A Comprehensive Foundation, 4th ed" (2003), Pearson Education (Sin- gapore) Pvt. Ltd
  14. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", Proc. Nat. Acad. Sci, 79(8), pp.2554~2558 (1982)
  15. D. Itchhaporiaa, "Artificial neural networks: current status in cardiovascular medicine", Journal of the American College of Cardiology, 28(2), pp.515~521 (1996)
  16. J-SR. Jang, "ANFIS: adaptive-network-based fuzzy inference system", IEEE trans. Syst. Man Cybern. Syst, 23(3), pp.665~685 (1993)
  17. J-SR. Jang, "Neuro-Fuzzy and Soft Computing, A computational Approach to learning and Machine Intelligence" (1997), Printece Hall
  18. J. Khan, "Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks", Nature medicine, 7(6), pp.673~679 (2001)
  19. T. Kohonen, "Self-organised formation of topologically correct feature maps", Bio. Cybern, 43(1), pp.59~69 (1982)
  20. L. D.Lascio, "A fuzzy based methodology for the analysis of diabetic neuropathy", Fuzzy Sets and Systems, 129, pp.203~228 (2002)
  21. P. J. Lisboa, "The use of artificial neural networks in decision support in cancer: A systematic review", Neural Networks, 19, pp.408~415 (2006)
  22. Y. Lu, "Region growing method for the analysis of functional MRI data", NeuroImage, 20(1), pp.455~465 (2003)
  23. M. Mahfouf, "A survey of fuzzy logic monitoring and control utilisation in medicine", Artif. Intell. Med, 21(1-3), pp.27~42 (2001)
  24. G. E. Matt, "Improving self-reports of drug-use: numeric estimates as fuzzy sets", Addiction, 98(9), pp.1239~1247 (2003)
  25. W. S. McCulloch, "A logical calculus of the ideas immanent in nervous activity", Bulletin of Math. Biophysics, 5, pp.115~133 (1943)
  26. J. N. Mordeson, "Fuzzy Mathematics in Medicine Heidelberg" (2000), Germany
  27. K. Polat, "An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease", Digit. Signal Process, 17(4), pp.702~710 (2007)
  28. D. E. Rummelhart, "Parallel distributed processing: Explorations in the microstructure of Cognition: Foundations" (1986), MIT Press
  29. J. Schneider, "Fuzzy logic-based tumor marker profiles including a new marker tumor M2- PK improved sensitivity to the detection of progression in lung cancer patients", Anticancer Research, 23(2A), pp.899~906 (2003)
  30. H. Seker, "A fuzzy logic based-method for prognostic decision making in breast and prostate cancers", IEEE Transactions on Information Technology in Biomedicine, 7(2), pp.114~122 (2003)
  31. A. Sharif, "Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting", Proceedings of the Seventh International Conference on Machine Learning and Cybernetics (2008)
  32. F. Steimann, "On the use and usefulness of fuzzy sets in medical AI", Artif. Intell. Med, 21(1-3), pp.131~137 (2001)
  33. P. S. Szczepaniak, "Fuzzy Systems in Medicine" (2000), Physica
  34. T. Takagi, "Derivation of fuzzy control rules from human operators control ac- tions", Proc. of the IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis pages, pp.55~60 (1983)
  35. H. Temurtas, "A comparative study on diabetes disease diagnosis using neural networks", Expert Syst. Appl, 36, pp.8610~8615 (2009)
  36. A. Torres, "Fuzzy Logic in Medicine and Bioinformatics", Jour- nal of Biomedicine and Biotechnology, 2006, pp.7~ (2006)[DOI]
  37. L. A. Zadeh, "Fuzzy Set", Information and Control, 8, pp.338~353 (1965)
  38. L. A. Zadeh, "Biological application of the theory of fuzzy sets and systems", The Proceedings of an International Symposium on Biocybernetics of the Central Nervous System, pp.199~206 (1969)
  39. L. A. Zadeh, "neural networks and soft computing, One-page course annuncement of CS 294-4", One-page course annuncement of CS 294-4 (1992), University of California ar Berkeley
  40. G. Zahlmann, "Hybrid fuzzy image processing for situation assessment", IEEE Engineering in Medicine and Biology Magazine, 19(1), pp.76~83 (2000)
  41. J. M. Zurada, "Introduction to Artificial Neural Systems", pp.121~ (1999), Jaico Publishing House

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