A Fuzzy-Neural-Genetic Algorithm Approach for Gene Expression and Micro Array Analysis for Breast Cancer Identification

A Fuzzy-Neural-Genetic Algorithm Approach for Gene Expression and Micro Array Analysis for Breast Cancer Identification

Velpula Nagi Reddy A. Rama Swamy Reddy

Research Scholar, Department of IT, VFSTR Deemed to be University, Vadlamudi, Guntur, India

Professor in CSE, VFSTR Deemed to be University, Vadlamudi, Guntur, India

Corresponding Author Email: 
nagireddy547@gmail.com, ramaswamyreddymail@gmail.com
Page: 
551-565
|
DOI: 
https://doi.org/10.18280/ama_b.600303
Received: 
26 December 2017
|
Accepted: 
3 January 2018
|
Published: 
31 September 2017
| Citation

OPEN ACCESS

Abstract: 

The machine assisted medicinal finding of intricate enclosed works, for example, Breast Cancer (BC) is measured as the most adamant hazard and the main source of death among ladies all in all. The superior part of the precedent work concerning this matter was on the study of disease transmission, learning of (BC) and routine with regards to breast self-examination (BSE), etiological variables, and rate of survival. Earlier detection of Breast Cancer saves enormous lives, crashing and burning which may provoke to other extraordinary issues expediting sudden deadly end. Its cure rate and desire depend predominantly on the early recognizable proof and finding of the contamination. A champion among the most surely understood sorts of remedial demonstrations of disregard universally is a screw up in determination. Today, there is immense use of information mining methods and process like Knowledge disclosure improvement for expectation. Critical taking in can be found from use of data mining techniques in social protection system. Untimely prediction and identification of Breast Cancer (BC) is a vital, certifiable therapeutic issue. In this paper, we suggest a genetic algorithm approach for the order of small scale cluster information utilizing quality expression to assemble a Breast Cancer (BC) analysis framework with high capacities and to predict the tumor accurately. A few investigations were directed utilizing these calculations. The accomplished forecast exhibitions are practically identical to existing systems. In any case, we discovered that proposed algorithm has a vastly improved execution than the different systems. The predictive investigation strategies of information mining assemble an information forecast display by examining the present history of patients in order to break down what's to come information. This paper audits the consequences of different arrangement systems that have been shown in the look into articles from quite a long while and makes a correlation between the displayed yields of the before works. In addition, experimentation is additionally directed to check the realness of the best clustering algorithm as a verification of idea. We concentrate on joining fuzzy-neural thoughts and Genetic algorithms to obviously create indicative structures to help and to comprehend and assess its outcomes with high characterization execution with accuracy. Our outcomes demonstrate that the proposed fuzzy-neural genetic qualities approach produces frameworks that accomplish high grouping execution, with basic and well interpretive standards and a decent level of certainty giving best results when compared with the available genetic algorithms.

Keywords: 

Breast cancer, Analysis troubles, Fuzzy-neural systems, Genetic algorithms, Machine assisted diagnosis

1. Introduction
2. Literature Survey
3. Fuzzy-Neural Systems Mechanisms
4. Proposed Genetic Algorithm
5. Results
6. Conclusion
  References

[1] Ying Xie, Department of Computer Science, Kennesaw State University, 1000 Chastain Road, Springer International Publishing Switzerland 2016.

[2] Mou'ath Hourani, Ibrahiem M. M. El Emary, ComSIS Vol. 6, No. 2, December 2009.

[3] BadrulSarwar, George Karypis, Joseph Konstan, John Riedl, ADVCOMP 2015: The Ninth International Conference on Advanced Engineering Computing and Applications in Sciences.

[4] Huihui Li, Changbo Zhao, Fengfeng Shao, Guo-Zheng Li1, Xiao Wang,From IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014), Belfast, UK. 2-5 November 2014.

[5] Gilles Meyer, Silv`ere Bonnabel, Rodolphe Sepulchre, 28th International Conference on Machine Learning, Bellevue, WA, USA, 2011.

[6] Constantinos Constantinopoulos and Aristidis Likas, IEEE Transactions on Neural Networks, 2007 IEEE.

[7]  A Fuzzy-neural Inference System Combined with Wavelet Transform for Breast Mass Classification by Pelin Gorgel, Ahmed Sertba and Osman N Ucan 2012, IEEE.

[8]  A New Lateral Guidance Device for Stereotactic Breast Biopsy Using an Add-on Unit to an Upright Mammography System, K. Ma, Member, IEEE, A. Fenster, Fellow, IEEE, A. Kornecki, Y. Mundt, J. Bax, 2008, IEEE. 

[9]  A Novel Neuro-Fuzzy-neural Classification System design by a Species-based hybrid Algorithm, by Ching-Hung Lee, Hsin-weichiu, and Chung-Ta Li, 2010, IEEE 

[10]  An Evolutionary Neuro-Fuzzy-neural Approach to Breast Cancer Diagnosis” by R. Ei Hamdi, M. Nijah, M. Chtourou, 2010 IEEE. 

[11] Based on Fuzzy-neural Linear Discriminant Analysis for Breast Cancer Mammography Analysis by Yu-Shun Cho, Chiun-Li Chin, Kun-Ching Wang, 2011, IEEE. 

[12]  O.L. Mangasarian, W.N. Street, W.H. Wolberg, Breast cancer diagnosis and prognosis via linear programming, MathematicalProgramming Technical Report 94-10, University of Wisconsin, 1994.

[13] O.L. Mangasarian, R. Setiono, W.H. Goldberg, Pattern recognition via linear programming: Theory and application to medicaldiagnosis, In: T.F. Coleman, Y. Li (eds.), Large-Scale Numerical Optimization, SIAM, 1990, pp. 22-31.

[14] C.J. Merz, P.M. Murphy, UCI repository of machine learning databases, http://www.ics.uci.edu, 1996.

[15] T. Nguyen, A. Khosravi, D. Creighton, Classification of healthcare data using genetic fuzzy-neural logic system and wavelets, 2015, Expert Systems with Applications, vol. 42, pp. 2184–2197.

[16]  C.-A. PenaReyes, M. Sipper, Evolving fuzzy-neural rules for breast cancer diagnosis, Proceedings of 1998 International Symposiumon Nonlinear Theory and Applications (NOLTA98), 2, Lausanne, Presses PolytechniquesetUniversitairesRomandes, 1998, pp. 369-372.

[17] R. Setiono, Extracting rules from pruned neural networks for breast cancer diagnosis, 1996, Artificial Intelligence in Medicine, pp. 37–51.

[18] I. Taha, J. Ghosh, Evaluation and ordering of rules extracted from feedforward networks, Proceedings of the IEEE InternationalConference on Networks, 1997, pp. 221–226.

[19] F. Tchier, Relational demonic fuzzy-neural refinement, Journal of Applied Mathematics, 2014, Article ID 140707, 17 pages.

[20] F. Tchier, Fuzzy-neural demonic refinement, 4th International Conference and Workshops on Basic and Applied Sciences and 11thRegional Annual Fundamental Science Symposium 2013 (ICOWOBAS-RAFSS 2013), Johor, Malaysia, September 3–5, 2013.

[21] P. Vuorimaa, Fuzzy-neural self-organizing map, Fuzzy-neural Sets and Systems 66 (1994) 223-231.

[22] R.R. Yager, D.P. Filev, Essentials of Fuzzy-neural Modeling and Control, Wiley, 1994.

[23] R.R. Yager, L.A. Zadeh, Fuzzy-neural Sets, Neural Networks, and Soft Computing, Van Nostrand Reinhold, New York, 1994.

[24] L.A. Zadeh, Fuzzy-neural logic and the calculus of fuzzy-neural if-then rules, Proceedings of the 22nd International Symposium on Multiple-Valued Logic, Los Alamitos, CA, IEEE Computer Society Press, 1992, pp. 480–490.

[25] L.A. Zadeh, Fuzzy-neural Sets, Information and Control 8, 1965, pp. 338–353.

[26] H.-J. Zimmermann, P. Zysno, Latent connectives in human decision making, Fuzzy-neural Sets and Systems, 4 (1980) 37–51.

[27] D.P. Garg, M. Kumar, Genetic Algorithm based PD Control and Fuzzy-neural Logic Control of a Two Link Robot, Paper No. IMECE2002-33433, Proceedings of ASME International Mechanical Engineering Congress and Exposition, 2002.

[28] B. Feil, J. Abonyi, F. Szeifert, Model order selection of nonlinear input-output models - a clustering based approach, Journal of Process Control, vol. 14, pp. 593-602, 2004. 

[29] X.L. Xie, G. Beni, A validity measure for fuzzy-neural clustering, IEEE Trans. Pattern Analysis and Machine, vol. 13, pp. 841-847, 1991. 

[30] M.B. Abdul Rahman, N. Chaibakhsh, M. Basri, Effect of alcohol structure on the optimum condition for novozym 435-catalyzed synthesis of adipate esters, Biotechnology Research International, In Press.

[31] E.L. Soo, A.B. Salleh, M. Basri, R.N.Z.R.A. Rahman, K. Kamaruddin, Response surface methodological study on lipase-catalyzed synthesis of amino acid surfactants, Process Biochemistry, vol. 39, pp. 1511-1518, 2004.

[32] E.R. Gunawan, M. Basri, M.B.A. Rahman, A.B. Salleh, R.N.Z.A. Rahman, Study on response surface methodology of lipase-catalyzed synthesis of palm-based wax ester, Enzyme and Microbial Technology, vol. 37, pp. 739-744, 2005.