About this Journal Submit a Manuscript Table of Contents
Advances in Fuzzy Systems
Volume 2012 (2012), Article ID 951247, 9 pages
http://dx.doi.org/10.1155/2012/951247
Research Article

Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction

1Department of Mechanical Engineering, Yuan Ze University, 32003 Chungli, Taiwan
2Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
3Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taoyuan City, Taiwan
4School of Engineering and Design, Brunel University, London, UK

Received 25 December 2011; Accepted 23 January 2012

Academic Editor: Hak-Keung Lam

Copyright © 2012 Yu-Tzu Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. O. Johnell and J. A. Kanis, “An estimate of the worldwide prevalence and disability associated with osteoporotic fractures,” Osteoporosis International, vol. 17, no. 12, pp. 1726–1733, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Cooper, E. J. Atkinson, S. J. Jacobsen, W. M. O'Fallon, and L. J. Melton, “Population-based study of survival after osteoporotic fractures,” American Journal of Epidemiology, vol. 137, no. 9, pp. 1001–1005, 1993. View at Scopus
  3. C. L. Leibson, A. N. A. Tosteson, S. E. Gabriel, J. E. Ransom, and L. J. Melton, “Mortality, disability, and nursing home use for persons with and without hip fracture: a population-based study,” Journal of the American Geriatrics Society, vol. 50, no. 10, pp. 1644–1650, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Magaziner, E. Lydick, W. Hawkes et al., “Excess mortality attributable to hip fracture in White women aged 70 years and older,” American Journal of Public Health, vol. 87, no. 10, pp. 1630–1636, 1997. View at Scopus
  5. G. S. Keene, M. J. Parker, and G. A. Pryor, “Mortality and morbidity after hip fractures,” British Medical Journal, vol. 307, no. 6914, pp. 1248–1260, 1993. View at Scopus
  6. A. M. Jette, B. A. Harris, P. D. Cleary, and E. W. Campion, “Functional recovery after hip fracture,” Archives of Physical Medicine and Rehabilitation, vol. 68, no. 10, pp. 735–740, 1987. View at Scopus
  7. J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony, “Structural risk minimization over data-dependent hierarchies,” IEEE Transactions on Information Theory, vol. 44, no. 5, pp. 1926–1940, 1998. View at Scopus
  8. C. Campbell, “Kernel methods: a survey of current techniques,” Neurocomputing, vol. 48, pp. 63–84, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Shawe-Taylor and S. Sun, “A review of optimization methodologies in support vector machines,” Neurocomputing, vol. 74, no. 17, pp. 3609–3618, 2011. View at Publisher · View at Google Scholar
  10. Y. C. Chou, Predicting the risk of hip bone fracture for elders in Taiwan by ensemble back-propagation neural networks, BS thesis, Department of Mechanical Engineering, University of Yuan Ze University, 2009.
  11. G. Li, H. Alnuweiri, Y. Wu, and H. Li, “Acceleration of back propagation through initial weight pre-training with delta rule,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 580–585, April 1993. View at Scopus
  12. Y. Lee, S. H. Oh, and M. W. Kim, “The effect of initial weights on premature saturation in back-propagation learning,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '91), pp. 765–770, July 1991. View at Scopus
  13. A. J. Al-Shareef and M. F. Abbod, “Neural networks initial weights optimisation,” in Proceedings of the 12th International Conference on Modelling and Simulation (UKSim '10), pp. 57–61, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Venkatesan, K. Kannan, and R. Saravanan, “A genetic algorithm-based artificial neural network model for the optimization of machining processes,” Neural Computing and Applications, vol. 18, no. 2, pp. 135–140, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. R. S. Sexton and J. N. D. Gupta, “Comparative evaluation of genetic algorithm and backpropagation for training neural networks,” Information Sciences, vol. 129, no. 1–4, pp. 45–59, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. D. J. Montana and L. Davis, “Training feedforward neural networks using genetic algorithms,” in Proceedings of the Joint Conference on Artificial Intelligence, vol. 1, pp. 762–767, 1989.
  17. B. G. Eriksson and V. Sundh, “Prediction of seven-year survival by artificial neural network and logistic regression: a comparison of results from medical and social data among 70-year-olds in Göteborg, Sweden,” Eriksson & Sundh, 2010.
  18. D. J. Montana, Neural Network Weight Selection Using Genetic Algorithms, Bolt Beranek and Newman, 1994.
  19. L. Davis, Ed., Genetic Algorithms and Simulated Annealing, Pitman, London, UK, 1991.
  20. D. E. Goldberg, Genetic Algorithms in Machine Learning, Addison-Wesley, 1988.
  21. T. Y. Lan, S. M. Hou, C. Y. Chen et al., “Risk factors for hip fracture in older adults: a case-control study in Taiwan,” Osteoporosis International, vol. 21, no. 5, pp. 773–784, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. L. M. Salchenberger, E. M. Cinar, and N. A. Lash, “Neural networks: a new tool for predicting thrift failures,” Decision Sciences, vol. 23, pp. 899–916, 1992.
  23. L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993–1001, 1990. View at Publisher · View at Google Scholar · View at Scopus
  24. P. Cunningham, J. Carney, and S. Jacob, “Stability problems with artificial neural networks and the ensemble solution,” Artificial Intelligence in Medicine, vol. 20, no. 3, pp. 217–225, 2000. View at Publisher · View at Google Scholar · View at Scopus
  25. W. S. Sarle, “Stopping training and other remedies for overfitting,” in Proceeding of the 27th Symposium on the Inter Face, 1995.
  26. F. Herrera, M. Lozano, and J. L. Verdegay, “Tackling real-coded genetic algorithms: operators and tools for behavioural analysis,” Artificial Intelligence Review, vol. 12, no. 4, pp. 265–319, 1998. View at Scopus
  27. L. J. Eshelman and J. D. Schaffer, “Real-coded genetic algorithms and interval-schemata,” Foundation of Genetic Algorithms, pp. 187–202, 1993.
  28. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1992.
  29. L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991.
  30. J. W. F. Catto, M. F. Abbod, D. A. Linkens, and F. C. Hamdy, “Neuro-fuzzy modeling: an accurate and interpretable method for predicting bladder cancer progression,” Journal of Urology, vol. 175, no. 2, pp. 474–479, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, pp. 33–37, 2007.