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Advances in Fuzzy Systems
Volume 2012 (2012), Article ID 951247, 9 pages
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.


This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of on modeling data and on testing data. They were slightly better than the results of our previous study ( and , resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.