The week's pick
Reseach Article
PCA-ANN for Classification of Hepatitis-C Patients
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 14 - Number 7 |
| Year of Publication: 2011 |
| Authors: Tahseen A. Jilani, Huda Yasin, Madiha Mohammad Yasin |
10.5120/1899-2530
|
Tahseen A. Jilani, Huda Yasin, Madiha Mohammad Yasin . PCA-ANN for Classification of Hepatitis-C Patients. International Journal of Computer Applications. 14, 7 ( February 2011), 1-6. DOI=10.5120/1899-2530
Abstract
In this paper, an automatic diagnosis system based on Neural Network for hepatitis virus is introduced. This automatic diagnosis system deals with the mixture of feature extraction and classification. The system has two stages, which are feature extraction – reduction and classification stages. In the feature extraction – diminution stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Missing values of the instances are adjusted using local mean method. Then, the number of these features was reduced to 6 from 19 due to relative significance of fields. In the classification stage, these reduced features are given as inputs Neural Network classifier. The classification accuracy of this ANN diagnosis system for the diagnosis of hepatitis virus was obtained, this accuracy was around 99.1% for training data and 100% for testing data.
References
- Watkins, A. (2001). AIRS: A resource limited artificial immune classifier; Master thesis, Mississippi State University. Available at: http://www.kidshealth.org/parent/infections/bacterial_viral/hepatitis.html#.
- Documentation Prepared by World Health Organization. Available at: http://www.who.int/csr/disease/hepatitis/HepatitisA_whocdscsredc2000_7.pdf.
- Bupa-Health and Life Care; available at: http://www.bupa.co.uk/individuals/.
- Information regarding hepatitis C from the staff of Mayo Clinic; available at: http://www.mayoclinic.com/health/hepatitis-c/DS00097.
- Causes of hepatitis C according to the staff of Mayo Clinic; available at: http://www.mayoclinic.com/health/hepatitis-c/DS00097/DSECTION=causes.
- Blake, C. L., & Merz, C. J. (1996), UCI repository of machine learning databases. Available from: http://www.ics.uci.edu./~mlearn/MLReporsitory.html.
- Cara Bruce, Lisa Montanarelli, M.D. Teresa Wright, and Teresa L. Wright, Hepatitis C: An Essential Guide for the Newly Diagnosed; 2nd edition, 2007.
- Mark Jenkins, Hepatitis C: practical, medical, and spiritual guidelines for daily living with HCV, 2000.
- Polat K., Gune S., “Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation”, Digital Signal Processing 16 (2006), pp. 889–901.
- Kedziora P., Figlerowicz M., Formanowicz P., Alejska M., Jackowiak P., Malinowska N., Fratczak A., Blazewicz J., and Figlerowicz M., “Computational Methods in Diagnostics of Chronic Hepatitis C”, Bulletin of the Polish Academy of Sciences, Technical Sciences, 53 (3), 2005, pp.273-281
- Hodgson S., Harrison R. F., Cross S. S., An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies, Image and Vision Computing 24, 2006, pp. 1025–1038.
- Peng Guan, De-Sheng Huang, Bao-Sen Zhou, “Forecasting model for the incidence of hepatitis A based on artificial neural network”, China World Journal of Gastroenterol; 10(24), 2004, pp. 3579-3582.
- Avendao R., Esteva L., Flores J. A., Fuentes Allen J. L., Gomez G., Lopez-Estrada J. E., A Mathematical Model for the Dynamics of Hepatitis C, Computational and Mathematical Methods in Medicine, Publisher by Taylor & Francis (Health-Sciences), 4(2), 2002, pp.109 — 118.
- Moneim I. A. and Mosa G. A., “Modeling the Hepatitis C with Different Types of Virus Genome”, Computational and Mathematical Methods in Medicine, 7(1), 2006, pp. 3-13.
- Information concerning hepatitis C; available at: http://www.medicalnewstoday.com/articles/145869.php
- Dunham M. H. and Sridhar S., Data Mining: Introductory and Advanced topics, Pearson Education, 2006
- Jang, Sun and Mizutani, Neuro-fuzzy logic and Soft Computing; A computational approach to learning and machine intelligence. New York: Practice-Hall, 2003, Chap. 2-4.
- Rosenblatt, Principles of Neuro-dynamics-An investigation on Perceptrons, 1962.
- Aqil Burney S.M., Jilani A. Tahseen and Cemal Ardil, “A comparative study of first and second order training algorithms for artificial neural networks”, Int. Journal of Computational Intelligence, vol. 1, no.3, 2004, pp. 218-224.
- Chen and Huang, “Dynamic Analysis of Stochastic Recurrent Neural Networks", 2003
- Y. Abu-Mostafa, A. Atiya, M. Magdon-Ismail, and H. White, "Guest Editorial: Special Issue on Neural Networks in Financial Engineering", IEEE Transactions on Neural Networks, Vol. 12, No. 4, July 2001, pp. 653-656.
- Kantardzic M., Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons, 2003, chapter 5.
- Battiti, Training Neural Nets with the Reactive Tabu Search, 1992
- Widrow and Hoff, Adaptive Switching Circuits, In Western Electronic Show and Convention, Volume 4, pages 96-104, Institute of Radio Engineers (now IEEE), 1960.
- Aqil Burney S.M., Jilani A. Tahseen and Saleemi, “Approximate Knowledge Extraction using MRA for TYPE-I Fuzzy Neural Networks”, 2006.
- Aqil Burney S.M., Jilani A. Tahseen and Cemal Ardil, “Approximate Bounded Knowledge Extraction using Type-I Fuzzy Logic”, 2005
- Dogantekin E., Dogantekin A.and Avci D., Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System, Expert Systems with Applications, 36, 2009, pp. 11282-11286.
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