Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

Ashraf Osman Ibrahim, Siti Mariyam Shamsuddin, Abdulrazak Yahya Saleh, Ali Ahmed, Mohd Arfian Ismail, Shahreen Kasim

Abstract


The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm.


Keywords


local search; breast cancer; neural network; NSGA-II; ANN.

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References


Khosrowshahi, F., Innovation in artificial neural network learning: Learn-On-Demand methodology. Automation in Construction, 2011. 20(8): p. 1204-1210.

Kuo, R. and L. Lin, Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems, 2010. 49(4): p. 451-462.

Cheok, C.Y., et al., Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Industrial Crops and Products, 2012. 40: p. 247-253.

Ding, S., et al., Evolutionary artificial neural networks: a review. Artificial Intelligence Review, 2013: p. 1-10.

Caballero, J.C.F., et al., Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks. IEEE Transactions on Neural Networks, 2010. 21(5): p. 750-770.

S. Dehuri, S. Patnaik, A. Ghosh, R. Mall, Application of elitist multi-objective genetic algorithm for classification rule generation. Applied Soft Computing Journal. 8, 477–487 (2008).

P. P. Bonissone, Y. U. T. O. Chen, K. Goebel, P. S. Khedkar, Hybrid soft computing systems: Industrial and commercial applications. Proceedings of the IEEE. 87, 1641–1667 (1999).

Seera, M. and C.P. Lim, A hybrid intelligent system for medical data classification. Expert Systems with Applications, 2014. 41(5): p. 2239-2249.

R. Deja, W. Froelich, G. Deja, A. Wakulicz-Deja, Hybrid approach to the generation of medical guidelines for insulin therapy for children. Information Sciences. 384, 157–173 (2017).

C. Y. Fan, P. C. Chang, J. J. Lin, J. C. Hsieh, A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Applied Soft Computing Journal. 11, 632–644 (2011).

Gorzałczany, M.B. and F. Rudziński, Interpretable and accurate medical data classification–a multi-objective genetic-fuzzy optimization approach. Expert Systems with Applications, 2017. 71: p. 26-39.

Zheng, B., S.W. Yoon, and S.S. Lam, Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 2014. 41(4): p. 1476-1482.

Turabieh, H., GA-based feature selection with ANFIS approach to breast cancer recurrence. International Journal of Computer Science Issues (IJCSI), 2016. 13(1): p. 36.

F. Ahmad, N. A. Mat Isa, Z. Hussain, M. K. Osman, S. N. Sulaiman, A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. Pattern Analysis and Applications. 18, 861–870 (2015).

A. O. Ibrahim, S. M. Shamsuddin, A. Y. Saleh, A. Abdelmaboud, A. Ali, in Proceedings - 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, ICCNEEE 2015 (Institute of Electrical and Electronics Engineers Inc., 2016), pp. 422–427.

L. Peng et al., An immune-inspired semi-supervised algorithm for breast cancer diagnosis. Computer methods and programs in biomedicine. 134, 259–65 (2016).

Lin, R.-H. and C.-L. Chuang, A hybrid diagnosis model for determining the types of the liver disease. Computers in Biology and Medicine, 2010. 40(7): p. 665-670.

H. Yan, Y. Jiang, J. Zheng, C. Peng, Q. Li, A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications. 30, 272–281 (2006).

Karegowda, A.G., A. Manjunath, and M. Jayaram, Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. International Journal on Soft Computing, 2011. 2(2): p. 15-23.

Karabatak, M. and M.C. Ince, An expert system for detection of breast cancer based on association rules and neural network. Expert systems with Applications, 2009. 36(2): p. 3465-3469.

Stoean, R. and C. Stoean, Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Systems with Applications, 2013. 40(7): p. 2677-2686.

Pettersson, F., N. Chakraborti, and H. Saxén, A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Applied Soft Computing, 2007. 7(1): p. 387-397.

Delgado, M., M.P. Cuellar, and M.C. Pegalajar, Multiobjective hybrid optimization and training of recurrent neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008. 38(2): p. 381-403.

Jin, Y., B. Sendhoff, and E. Körner. Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. in International Conference on Evolutionary Multi-Criterion Optimization. 2005. Springer.

Liu, G. and V. Kadirkamanathan, Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proceedings-Control Theory and Applications, 1999. 146(5): p. 373-382.

Mane, S., S. Sonawani, and S. Sakhare, Hybrid Multi-objective Optimization Approach for Neural Network Classification Using Local Search, in Innovations in Computer Science and Engineering. 2016, Springer. p. 171-179.

Ibrahim, A.O., S. Hasan, and S. Noman, Memetic Elitist Pareto evolutionary algorithm of three-term backpropagation network for classification problems. Int. J. Advance Soft Compu. Appl, 2014. 6(3).

A. O. Ibrahim, S. M. Shamsuddin, N. B. Ahmad, M. N. M. Salleh, in 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings (Institute of Electrical and Electronics Engineers Inc., 2014).

Xiaoyuan, L., Q. Bin, and W. Lu. A New Improved BP Neural Network Algorithm. in Intelligent Computation Technology and Automation, 2009. ICICTA'09. Second International Conference on. 2009. IEEE.

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6, 182–197 (2002).

Coello, C.A., An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys (CSUR), 2000. 32(2): p. 109-143.

A. Lara, G. Sanchez, C. A. C. Coello, O. Schütze, HCS: A new local search strategy for memetic multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation. 14, 112–132 (2010).

De Jong, K.A. and W.M. Spears, A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 1992. 5(1): p. 1-26.

Črepinšek, M., S.-H. Liu, and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 2013. 45(3): p. 35.

Wolberg, W.H. and O.L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proceedings of the national academy of sciences, 1990. 87(23): p. 9193-9196.

Qasem, S.N., et al., Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems. Information Sciences, 2013. 239: p. 165-190.

Abbass, H.A., An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial intelligence in Medicine, 2002. 25(3): p. 265-281.

Ibrahim, A.O., et al., Three-Term Backpropagation Network based on elitist multiobjective genetic algorithm for medical diseases diagnosis classification. Life Science Journal, 2013. 10(4): p. 1815-1822.




DOI: http://dx.doi.org/10.18517/ijaseit.9.2.4986

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