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Article:Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 7 - Number 13 |
| Year of Publication: 2010 |
| Authors: Shaikh Abdul Hannan, R. R. Manza, R. J. Ramteke |
10.5120/1325-1799
|
Shaikh Abdul Hannan, R. R. Manza, R. J. Ramteke . Article:Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis. International Journal of Computer Applications. 7, 13 ( October 2010), 7-13. DOI=10.5120/1325-1799
Abstract
In this paper, two types of Artificial Neural Network (ANNs), Generalized Regression Neural Network (GRNN) and Radial Basis Function (RBF) have been used for heart disease to prescribe the medicine. Diagnosing the heart disease and prescribing the medicine on the basis of symptoms is a very challenging task to improve the ability of the physicians. The training capacity and medicines provided by these two techniques are compared with the original medicines provided by the heart specialist. About 300 patients data are collected from Sahara Hospital, Aurangabad under the supervision of doctor. This study includes the detailed information about patient and preprocessing was done. The GRNN and RBF have been applied over this patient data for the outcome the medicine. The result of these evaluation show that the overall performance of RBF can be applied successfully for prescribing the medicine for the heart disease patient.
References
- CDC’s report, http://www.cdc.gov/nccdphp/overview.htm.
- Long, W. J., Naimi, S., & Criscitello, M. G. (1992). Development of a knowledge base for diagnostic reasoning in cardiology. Computers in Biomedical Research, 25, 292–311.
- Azuaje, F., Dubitzky, W., Lopes, P., Black, N., & Adamsom, K. (1999). Predicting coronary disease risk based on short-term RR interval measurements: A neural network approach. Artificial Intelligence in Medicine, 15, 275–297.
- Tkacz, E. J., & Kostka, P. (2000). An application of wavelet neural network for classification patients with coronary artery disease based on HRV analysis. Proceedings of the Annual International Conference on IEEE Engineering in Medicine and Biology , 1391–1393.
- Reategui, E. B., Campbell, J. A., & Leao, B. F. (1997). Combining a neural network with case-based reasoning in a diagnostic system. Artificial Intelligence in Medicine, 9, 5–27.
- Tsai, D. Y., & Watanabe, S. (1998). Method optimization of fuzzy reasoning by genetic algorithms and its application to discrimination of myocardial heart disease. Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference, 1756–1761.
- . http://www.ifcc.org/ejifcc/vol14no2/140206200308n.htm
- Perlovsky LI. Neural networks and intellect. Oxford University, Press; 2001.
- Haque ME, Sudhakar KV. ANN back propagation prediction model for fracture toughness in microalloy steel. Int J Fatique 2002;24:1003–10.
- Celikoglu HB. Application of radial basis function and eneralized regression neural networks in non-linear utility function specification for travel mode choice modelling. Math Comput Model 2006;44:640–58
- Celikoglu HB, Cigizoglu HK. Public transportation trip flow modeling with generalized regression neural networks. Adv Eng Software 2007;38:71–9.
- Cigizoglu HK, Alp M. Generalized regression neural network in modelling river sediment yield. Adv Eng Software 2005;37:63–8.
- Kim B, Lee DW, Parka KY, Choi SR, Choi S. Prediction of plasma etching using a randomized generalized regression neural network. Vacuum 2004;76:37–43
- Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, New Jersey, USA; 1997 [Chapter 9].
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