A comparative study on diabetes disease diagnosis using neural networks

Expert Systems with Applications

Volumn 36, Issue 4, 2009, Pages 8610-8615

  Temurtas, Hasan ^a   Yumusak, Nejat ^b   Temurtas, Feyzullah ^b,c  

^a DUMLUPINAR UNIVERSITY   (Turkey)

^b SAKARYA UNIVERSITY   (Turkey)

^c BOZOK UNIVERSITY   (Turkey)

Author keywords

Diabetes disease diagnosis; Levenberg Marquardt algorithm; Multilayer neural network; Probabilistic neural network

Indexed keywords

BLOOD VESSELS; GLUCOSE; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; MULTILAYERS;

COMPARATIVE STUDIES; DIABETES DISEASE DIAGNOSIS; HEART DISEASE; KIDNEY DISEASE; LEVENBERG-MARQUARDT ALGORITHM; MACHINE-LEARNING DATABASE; MULTILAYER NEURAL NETWORK; NERVE DAMAGES; NEURAL NETWORK STRUCTURES; PROBABILISTIC NEURAL NETWORK; VESSEL DAMAGES;

NEURAL NETWORKS;

EID: 60349107274     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.10.032     Document Type: Article

References (31)

1. Acharya U.R., Tan P.H., Subramaniam T., et al. Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph. Journal of Medical Systems 32 1 (2008) 21-29
2. Baier L.J., and Hanson R.L. Genetic studies of the etiology of type 2 diabetes in Pima Indians. Diabetes 53 (2004) 1181-1186
3. Brent R.P. Fast training algorithms for multi-layer neural nets. IEEE Transactions on Neural Networks 2 (1991) 346-354
4. Campbell N.A. Robust procedures in multivariate analysis 1: Robust covariance estimation. Applied Statistics 29 (1980) 231-237
5. Carpenter G.A., and Markuzon N. ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases. Neural Networks 11 (1998) 323-336
6. Chen L.H., and Chang S. An adaptive learning algorithm for principal component analysis. IEEE Transactions on Neural Networks 6 5 (1995) 1255-1263
7. Daszykowski M., Kaczmarek K., Vander H.Y., and Walczak B. Robust statistics in data analysis - A review basic concepts. Chemometrics and Intelligent Laboratory Systems 85 (2007) 203-219
8. Delen D., Walker G., and Kadam A. Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine Artificial Intelligence in Medicine 34 2 (2005) 113-127
9. Deng, D., & Kasabov, N. (2001). On-line pattern analysis by evolving self-organizing maps. In Proceedings of the fifth biannual conference on artificial neural networks and expert systems (ANNES) (pp. 46-51).
10. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. G. R. (1996). Advances in knowledge discovery and data mining. Menlo Park, CA: AAAI Press/The MIT Press.
11. Gori M., and Tesi A. On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (1992) 76-85
12. Gulbag, A. (2006). Dagaalty work ** . Ph.D. thesis, Sakarya University, Institute of Science & Technology.
13. Gulbag A., and Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sensors and Actuators B 115 (2006) 252-262
14. Gulbag A., Temurtas F., and Yusubov I. Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks. Sensors and Actuators B: Chemical 131 (2007) 196-204 10.1016/j.snb.2007.11.008
15. Hagan M.T., Demuth H.B., and Beale M.H. Neural network design (1996), PWS Publishing
16. Hagan M.T., and Menhaj M. Training feed forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 (1994) 989-993
17. Hoshi K., Kawakami J., Kumagai M., Kasahara S., Nisimura N., Nakamura H., et al. An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks. Chemical & Pharmaceutical Bulletin 53 (2005) 1570-1574
18. Jang J.-S.R., Sun C.-T., and Mizutani E. Neuro_Fuzzy and soft computing. (p. 07458) (1997), Prentice Hall, Upper Sllade River, NJ
19. Kayaer, K., & Yi{dotless}ldi{dotless}ri{dotless}m, T. (2003). Medical diagnosis on Pima Indian diabetes using general regression neural networks. In Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP) (pp. 181-184).
20. Knowler W.C., Pettitt D.J., Saad M.F., Charles M.A., Nelson R.G., Howard B.V., et al. Obesity in the Pima Indians: Its magnitude and relationship with diabetes. American Journal of Clinical Nutrition 53 (1991) 1543-1551
21. Matlab® documentation. (2004). Version 7.0, release 14. The MathWorks Inc.
22. Mohamed E.I., Linderm R., Perriello G., Di Daniele N., Poppl S.J., and De Lorenzo A. Predicting type 2 diabetes using an electronic nose-base artificial neural network analysis. Diabetes Nutrition & Metabolism 15 4 (2002) 215-221
23. Polat K., and Gunes S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing 17 4 (2007) 702-710
24. Polat K., Gunes S., and Aslan A. A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert Systems with Applications 34 1 (2008) 214-221
25. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel distributed processing: Explorations in the microstructure of cognition Vol. 1 (1986), MIT Press, Cambridge, MA 318-362
26. Serneels S., and Verdonck T. Principal component analysis for data containing outliers and missing elements. Computational Statistics & Data Analysis 52 (2008) 1712-1727
27. Specht D.F. Probabilistic neural networks. Neural Networks 3 (1990) 109-118
28. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics (1985) 116-132
29. Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications 36 (2009) 944-949
30. Torre, F. D., & Black, M. J. (2001). Robust principal component analysis for computer vision. In Proceedings of the eighth international conference on computer vision (ICCV'01) (Vol. 1, pp. 362-369).
31. Watkins, A. (2001). AIRS: A resource limited artificial immune classifier. Master thesis, Mississippi State University.