Diabetes classification using Radial Basis Function Network by combining cluster validity index and BAT optimization with novel fitness function

International Journal of Computational Intelligence Systems

Volumn 10, Issue 1, 2017, Pages 247-265

  Cheruku, Ramalingaswamy ^a   Edla, Damodar Reddy ^a   Kuppili, Venkatanareshbabu ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY GOA   (India)

Author keywords

Bat Algorithm; Classification; Diabetes; Medical diagnosis; Optimal number of clusters; Radial Basis Function Networks

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; COMPUTER AIDED DIAGNOSIS; CONFORMAL MAPPING; DIAGNOSIS; FUNCTIONS; IMAGE SEGMENTATION; ITERATIVE METHODS; LEARNING ALGORITHMS; MEDICAL PROBLEMS; NETWORK LAYERS; NEURAL NETWORKS; OPTIMIZATION; TIME DELAY; TWO PHASE FLOW;

BAT ALGORITHMS; CLUSTER VALIDITY INDICES; OPTIMAL NUMBER; OPTIMIZATION ALGORITHMS; PIMA INDIANS DIABETES DATA SETS; PROBABILISTIC NEURAL NETWORKS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS NEURAL NETWORKS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 85018744114     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.2991/ijcis.2017.10.1.17     Document Type: Article

References (34)

1. World Health Organization: http://www.who.int/ diabetes/action_online/basics/en.
2. World Health Organization: http://www.who.int/ diabetes/facts/en/.
3. K. Rahul, V. Harsh, N. Khanwalkar, and M. Bhattacharya, Evolutionary Radial Basis Function Network for Classificatory Problems, Int. J. of Comp. Sci. and Appl., 7 (4) (2010) 34-49.
4. M. Halkidi, Y. Batistakis, and M. Vazirgiannis, Clustering algorithms and validity measures, in Proc. 13th Int. Conf. Scientific and Stat. Database Management, (2001), pp. 3-22.
5. J. Moody and J. Darken, Fast learning in networks of locally-tuned processing units, J.Neu.Comput. 1 (2) (1989) 281-294.
6. R. Siddheswar and R. H Turi, Determination of number of clusters in k-means clustering and application in colour image segmentation, in Proc. 4th Int. Conf. Advan. Patte. Recog. Digital techniques,(Calcutta, India, 1999), pp. 137-143.
7. S. Haykin, Neural networks and learning machines, 3nd edn. (Prentice Hall, NJ, 2009).
8. J. Shen, S. I. Chang, E. S. Lee, Y. Deng, and S. J. Brown, Determination of cluster number in clustering microarray data, J. App. Math and Computation 169 (2) (2005) 1172-1185.
9. M. Koklu and Y. Unal, Analysis of a Population of Diabetic Patients Databases with Classifiers, in Proc. of World Academy of Science, Engineering and Technology, (2013).
10. S. W. Purnami, E. Abdullah, J. M. Zain, and S. P. Rahayu, A new smooth support vector machine and its applications in diabetes disease diagnosis, J. Comp. Sci. 5 (12) (2009) 1003-1008.
11. O. S. Soliman and E. AboElhamd, Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine, Int. Jou. of Comp. Trends and Tech. 8 (1) (2014) 38-44.
12. M. R. Bozkurt, N. YURTAY, Z. YILMAZ, and C. SERTKAYA, Comparison of different methods for determining diabetes, Turkish J. of Elect. Engg. and Comp. Sci. 22 (4) (2014) 1044-1055.
13. H. M. Fiuzy, A. Qarehkhani, and J. Haddadnia, Introduction of a method to diabetes diagnosis according to optimum rules in fuzzy systems based on combination of data mining algorithm (dt), evolutionary algorithms (aco) and artificial neural networks (nn), J. of Math. and Comp. (JMCS) 6 (4) (2013) 272-285.
14. R. Radha and S. P. Rajagopalan, Fuzzy logic approach for diagnosis of diabetics, Infor. Tech. Jou. 6 (1) (2007) 96-102.
15. A. G. Karegowda, A. S. Manjunath, and M. A. Jayaram, Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes, Int. Jour. on Soft Comp. 2 (2) (2011) 15-23.
16. M. Khashei, S. Eftekhari, and J. Parvizian, Diagnosing diabetes type ii using a soft intelligent binary classification model, Rev. of Bioinformatics and Biometrics 1 (1) (2012) 9-23.
17. P. Jeatrakul and K.W. Wong, Comparing the performance of different neural networks for binary classification problems, in Sym. 8th IEEE Int. Sym. Natural Lang. Processing,(2009), pp. 111-115.
18. Y. J. Oyang, S. C. Hwang, Y. Y. Ou, C.Y. Chen, and Z. W. Chen, Data classification with radial basis function networks based on a novel kernel density estimation algorithm, IEEE Transactions on Neural Networks 16 (1) (2005) 225-236.
19. P. Venkatesan and S. Anitha, Application of a radial basis function neural network for diagnosis of diabetes mellitus, Current Science 91 (9) (2006) 1195-1199.
20. Shankaracharya, D. Odedra, S. Samanta, and A. S. Vidyarthi, Computational intelligence in early diabetes diagnosis: a review, Rev. Diabet. Stud. 7 (4) (2010) 252-262.
21. K. Tyagi, X. Cai, and M. T. Manry, Fuzzy C-means clustering based construction and training for second order RBF network, in IEEE Int. Conf. on Fuzzy Systems, (Taipei, Taiwan, 2011), pp. 248-255.
22. W. Pedrycz, Conditional fuzzy clustering in the design of radial basis function neural networks, IEEE Transactions on Neural Networks 9 (4) (1998) 601-612.
23. J. Marco and J. V. Snchez-Andrs, Improving the LBG algorithm, in Int. Work-Conf. on Artificial and Natural Neural Networks, (Alicante, Spain, 1999), pp. 621-630.
24. J. Peñalver and González, Identificación y optimización de redes de funciones base radiales para aproximación funcional, (Universidad de Granada, 2001).
25. R. O. Duda and P. E. Hart, Pattern classification and scene analysis, (Wiley, New York, 1973).
26. T. A. Runkler and J. C. Bezdek, Alternating cluster estimation: a new tool for clustering and function approximation, IEEE Transactions on Fuzzy Systems 7 (4) (1999) 377-393.
27. X. S. RYang, A new metaheuristic bat-inspired algorithm, in Nature inspired cooperative strategies for optimization, (Springer, New York, 2010), pp. 65-74.
28. P. M. Taha and ALICIA Y.C. TANG, Bat algorithm for rough set attribute reduction, J. of Theoretical and Applied Info. Tech. 51 (1) (2013) 1-8.
29. P. Prashanth, K. K. Pattanaik, and P.S. Rana, BAT and Hybrid BAT Meta-Heuristic for Quality of Service-BasedWeb Service Selection, J. of Intelligent Systems (2016)
30. P. Golik, P. Doetsch, and H. Ney, Cross-entropy vs. Squared error training: a theoretical and experimental comparison, in INTERSPEECH, (2003), pp. 1756-1760.
31. Using Entropy for Evaluating and Comparing Probability Distributions: http://www.cs.rochester.edu/u/james/CSC248/Lec6.pdf
32. UCI Repositary: http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes.
33. K. Ganapathy, V. Vaidehi and J. B. Chandrasekar, Optimum steepest descent higher level learning radial basis functionnetwork, Expert Sys. and Applications 42 (21) (2015) 8064-8077.
34. D. P. Ferreira Cruz, R. D. Maia, L. A. da Silva and L. N. de Castro, BeeRBF: A bee-inspired data clustering approach to design RBF neuralnetwork classifiers, Neurocomputing 172 (2016) 427-437.