DOI QR코드

DOI QR Code

Using a Genetic-Fuzzy Algorithm as a Computer Aided Breast Cancer Diagnostic Tool

  • Alharbi, Abir (Mathematics Department, King Saud University) ;
  • Tchier, F (Mathematics Department, King Saud University) ;
  • Rashidi, MM (Department of Mechanical Engineering, Tongji University)
  • Published : 2016.07.01

Abstract

Computer-aided diagnosis of breast cancer is an important medical approach. In this research paper, we focus on combining two major methodologies, namely fuzzy base systems and the evolutionary genetic algorithms and on applying them to the Saudi Arabian breast cancer diagnosis database, to aid physicians in obtaining an early-computerized diagnosis and hence prevent the development of cancer through identification and removal or treatment of premalignant abnormalities; early detection can also improve survival and decrease mortality by detecting cancer at an early stage when treatment is more effective. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized systems that attain high classification performance, with simple and readily interpreted rules and with a good degree of confidence.

Keywords

Fuzzy systems;genetic algorithms;optimization methods;breast cancer;computer aided diagnosis

Acknowledgement

Grant : Research Centre of the Female Scientific and Medical Colleges

Supported by : King Saud University

References

  1. Alander JT (1997). An indexed bibliography of genetic algorithms with fuzzy logic Fuzzy. Fuzzy evolutionary computation. Springer, USA, 299-318.
  2. AlDiab R, Qureshi S, AlSaleh KA, et al (2013). Studies on the methods of diagnosis and biomarkers used in the early detection of breast cancer in the kingdom of saudi Arabia. World J Med Sci, 5, 72-88.
  3. Al Diab R, Qureshi S, Khalid A, et al (2013). Review on breast cancer in the kingdom of Saudi Arabia. Middle East J Scientific Res, 14, 532-43.
  4. Alharbi A, Rand W, Rolio R, et al (2007), Understanding the semantics of genetic algorithms in dynamic environments; a case study using the shaky ladder hyperplane-defined functions, workshop on evolutionary algorithms in stochastic and dynamic environments, incorporated in evo conferences Valencia, Spain.
  5. Andres C, Reyes P, Sipper M, et al (1999). A genetic-fuzzy approach to breast cancer diagnosis. Artificial Intelligence Med, 17, 131-55. https://doi.org/10.1016/S0933-3657(99)00019-6
  6. Carmona J, Ruiz-Rodado V, del Jesus MJ, et al (2015). A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans. Informat Sci, 298, 180-97. https://doi.org/10.1016/j.ins.2014.11.030
  7. Cordon O, Herrera F, Lozano M, et al (1997). On the combination of fuzzy logic and evolutionary computation: a short review and bibliography. Fuzzy Evolutionary Computation, 1, 33-56.
  8. Dennis B, Muthukrishnan S (2014). GFS: Adaptive Genetic Fuzzy System for medical data classification, Applied Soft Computing, Elsevier, 25, 242-52. https://doi.org/10.1016/j.asoc.2014.09.032
  9. El-Akkad SM, Amer M.H, Lin GS, et al (1986). Pattern of cancer in Saudi Arabia. King Faisal Specialist Hospital Cancer J, 58, 1172-8.
  10. Heider H, Drabe T,(1997).Fuzzy system design with a cascaded genetic algorithm. IEEE Int Conference Evolutionary Computat, 1, 585-8.
  11. Ferlay J, Soerjomataram I, Ervik M, et al (2012). cancer incidence and mortality worldwide: iarc cancer base no. 11, lyon, france: international agency for research on cancer; 2013.
  12. Herrera F, Lozano M, Verdegay JL, et al (1995). Generating fuzzy rules from examples using genetic algorithms. Fuzzy Logic and Soft Computing. World Scientific, 1, 11-20.
  13. Jang JR, Sun CT (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83, 378-406. https://doi.org/10.1109/5.364486
  14. Karr CL (1991). Genetic algorithms for fuzzy controllers, A I Expert, 6, 26-33.
  15. Kovalerchuk B, Triantaphyllou E, Ruiz JF, et al (1997). Fuzzy logic in computer-aided breast cancer diagnosis. Artificial Intelligence Medical, 11, 75-85. https://doi.org/10.1016/S0933-3657(97)00021-3
  16. Koza J R, (1992), Genetic Programming, USA, MIT Press.
  17. Lee M A, Takagi H, (1993). Integrating design stages of fuzzy systems using genetic algorithms. IEEE International Conference on Fuzzy Systems, 1, 612-7.
  18. Mangasarian OL, Street WN, Wolberg WH, et al (1994). Breast cancer diagnosis and prognosis via linear programming. Mathematical Programming Technical Report, 94, 94-10.
  19. Matlab Tool Box Guide retrieved Jan 2015 from http://www.mathworks.com/products/global-optimization/features.html#genetic-algorithm-solver.
  20. Mendel J M, (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83, 345-377. https://doi.org/10.1109/5.364485
  21. Merz CJ, Murphy PM (1996). UCI repository of machine learning-databases. http://www.ics.uci.edu/-mlearn/MLR repository.
  22. Michalewicz Z, (1996).Genetic Algorithms Data Structures, Evolution Programs, 3rd edition, Berlin, Springer-Verlag.
  23. Nguyen T, Khosravi A, Creighton D, et al (2015). Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Systems with Applications, 42, 2184-97. https://doi.org/10.1016/j.eswa.2014.10.027
  24. Rashidi M M, Anwar O, Beg A, Basiriparsa, Nazari F, et al (2011). Analysis and optimization of a trans critical power cycle with regenerator using artificial neural networks and genetic algorithms, proceedings of the institution of mechanical engineers. Part A: J Power Energy, 225, 701-17. https://doi.org/10.1177/0957650911407700
  25. Setiono R, (1996). Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine, 8, 37-51. https://doi.org/10.1016/0933-3657(95)00019-4
  26. Tchier F (2014). Relational demonic fuzzy refinement. J Applied Mathematics, 2014, 1-17.
  27. Tchier F (2013). Fuzzy demonic refinement. international conference on basic and applied sciences regional annual fundamental science symposium 2013, Johor, Malaysia.
  28. Vuorimaa P (1994). Fuzzy self-organizing map. Fuzzy Sets Systems, 66, 223-31. https://doi.org/10.1016/0165-0114(94)90312-3
  29. Yager R R, Filev D P, (1994). Essentials of Fuzzy Modeling and Control, Canada, Wiley.
  30. Yager RR, Zadeh LA (1994). Fuzzy sets neural networks and soft computing. New York, Van Nostrand Reinhold.