Ovarian cancer prognosis and soft computing

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 1, Issue , 2002, Pages 60-61

  Odusanya, A A ^a  

^a Institute for Clean Growth and Future Mobility   (United Kingdom)

Author keywords

Classifier; Ovarian cancer; Prognosis; Soft computing

Indexed keywords

BACKPROPAGATION; FEEDFORWARD NEURAL NETWORKS; FUZZY SETS; GENETIC ALGORITHMS; MEMBERSHIP FUNCTIONS; PATIENT MONITORING; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

MULTILINEAR REGRESSION ANALYSIS; OVARIAN CANCER PROGNOSIS; SOFT COMPUTING;

ONCOLOGY;

EID: 0036907850     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. P. J. F. Lucas, and A. Abu-Hanna, "Prognostic methods in medicine," Artificial Intelligence in Medicine, vol. 15, pp. 105-119, February 1999.
2. H. C. Kitchener, "Gynaecological cancer," Postgrad. Med. J., vol. 75, pp. 332-338, 1999.
3. O. Kaynak, and I. Rudas, "Soft computing methodologies and their fusion in mechatronic products," Computing and Control Engineering Journal, vol. 6, pp.68-72, April 1995.
4. J. A. Freeman, and D. M. Skapura, Neural networks algorithms, applications, and programming techniques, 1st ed., Addison-Wesley Publishing Company, Inc., 1991.
5. H. Takagi, "Introduction to fuzzy systems, neural networks, and genetic algorithms," in Intelligent hybrid systems. Fuzzy logic, neural networks, and genetic algorithms, D. Ruan, Ed. Kluwer Academic Publishers, 1997, pp. 3-33.
6. L. A. Zadeh, "Fuzzy sets", Information and Control, vol. 8, pp. 338-353, 1965.
7. E. Cox, The fuzzy systems handbook. A practitioner's guide to building, using, and maintaining fuzzy systems, Academic Press, Inc., 1994.
8. W. Pedrycz, and F. Gomide, An introduction to fuzzy sets. Analysis and design, Massachussetts Institute of Technology, 1998.
9. T. Bäck, Evolutionary algorithms in theory and practice: evolutionary strategies evolutionary programming genetic algorithms, Oxford University Press, 1996.
10. D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley, 1989.
11. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
12. A. A. Odusanya, M. O. Odetayo, R. N. G. Naguib, and D. Petrovic, "A review of soft computing and gynaecological cancer," in Quo Vadis Computational Intelligence? P. Sincak, and J. Vascak, Eds. Springer-Verlag Company, 2000, pp. 485-490.
13. H. J. Kappen, and J. P. Neijt, "Neural network analysis to predict treatment outcome," Annals of Oncology, vol. 4, pp. S31-S34, 1993.
14. R. Biagiotti, C. Desli, E. Vanzi, and G. Gacci, "Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color doppler flow US," Radiology, vol. 210, pp. 399-403, 1999.
15. A. A. Odusanya, M. O. Odetayo, D. Petrovic, and R. N. G. Naguib, "The use of evolutionary and fuzzy models in oncological prognosis" in Developments in Soft Computing, R. John, and R. Birkenhead, Eds. Springer-Verlag, 2001, pp. 207-215.
16. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.