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http://dx.doi.org/10.14456/apjcp.2016.150/APJCP.2016.17.7.3651

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)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.17, no.7, 2016 , pp. 3651-3658 More about this Journal
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;
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  • Reference
1 Karr CL (1991). Genetic algorithms for fuzzy controllers, A I Expert, 6, 26-33.
2 Kovalerchuk B, Triantaphyllou E, Ruiz JF, et al (1997). Fuzzy logic in computer-aided breast cancer diagnosis. Artificial Intelligence Medical, 11, 75-85.   DOI
3 Koza J R, (1992), Genetic Programming, USA, MIT Press.
4 Lee M A, Takagi H, (1993). Integrating design stages of fuzzy systems using genetic algorithms. IEEE International Conference on Fuzzy Systems, 1, 612-7.
5 Alander JT (1997). An indexed bibliography of genetic algorithms with fuzzy logic Fuzzy. Fuzzy evolutionary computation. Springer, USA, 299-318.
6 Merz CJ, Murphy PM (1996). UCI repository of machine learning-databases. http://www.ics.uci.edu/-mlearn/MLR repository.
7 Mangasarian OL, Street WN, Wolberg WH, et al (1994). Breast cancer diagnosis and prognosis via linear programming. Mathematical Programming Technical Report, 94, 94-10.
8 Matlab Tool Box Guide retrieved Jan 2015 from http://www.mathworks.com/products/global-optimization/features.html#genetic-algorithm-solver.
9 Mendel J M, (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83, 345-377.   DOI
10 Michalewicz Z, (1996).Genetic Algorithms Data Structures, Evolution Programs, 3rd edition, Berlin, Springer-Verlag.
11 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.   DOI
12 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.   DOI
13 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.
14 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.
15 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.
16 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.
17 Andres C, Reyes P, Sipper M, et al (1999). A genetic-fuzzy approach to breast cancer diagnosis. Artificial Intelligence Med, 17, 131-55.   DOI
18 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.   DOI
19 Dennis B, Muthukrishnan S (2014). GFS: Adaptive Genetic Fuzzy System for medical data classification, Applied Soft Computing, Elsevier, 25, 242-52.   DOI
20 Tchier F (2014). Relational demonic fuzzy refinement. J Applied Mathematics, 2014, 1-17.
21 Tchier F (2013). Fuzzy demonic refinement. international conference on basic and applied sciences regional annual fundamental science symposium 2013, Johor, Malaysia.
22 Vuorimaa P (1994). Fuzzy self-organizing map. Fuzzy Sets Systems, 66, 223-31.   DOI
23 Yager R R, Filev D P, (1994). Essentials of Fuzzy Modeling and Control, Canada, Wiley.
24 Yager RR, Zadeh LA (1994). Fuzzy sets neural networks and soft computing. New York, Van Nostrand Reinhold.
25 Setiono R, (1996). Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine, 8, 37-51.   DOI
26 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.
27 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.
28 Heider H, Drabe T,(1997).Fuzzy system design with a cascaded genetic algorithm. IEEE Int Conference Evolutionary Computat, 1, 585-8.
29 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.
30 Jang JR, Sun CT (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83, 378-406.   DOI