Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

This research was financially supported by the Ministry of Science and Technology, Taiwan (Grant number: NSC102-2221-E-155-028-MY3), and sponsored by China Scholarship Council, China (CSC, File No. 2010695013). This research was also supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology, Taiwan (Grant number: NSC 102-2911-I-008-001).

[1]  Chen-chiang Lin,et al.  Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture. , 2010, Injury.

[2]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[3]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[4]  Jiann-Shing Shieh,et al.  Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition. , 2009, Medical engineering & physics.

[5]  S. Boonen,et al.  Costs and consequences of hip fracture occurrence in old age: An economic perspective , 2005, Disability and rehabilitation.

[6]  J. Knottnerus,et al.  A risk model for the prediction of recurrent falls in community-dwelling elderly: a prospective cohort study. , 2002, Journal of clinical epidemiology.

[7]  Maysam F. Abbod,et al.  Diffuse large B-cell lymphoma classification using linguistic analysis and ensembled artificial neural networks , 2012 .

[8]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[9]  Eve Donnelly,et al.  The assessment of fracture risk. , 2010, The Journal of bone and joint surgery. American volume.

[10]  Oscar Castillo,et al.  Optimization of type-2 fuzzy integration in ensemble neural networks for predicting the US Dolar/MX pesos time series , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[11]  Sheryl Zimmerman,et al.  Changes in functional status attributable to hip fracture: a comparison of hip fracture patients to community-dwelling aged. , 2003, American journal of epidemiology.

[12]  J. Magaziner,et al.  Hip fracture: Risk factors and outcomes , 2003, Current osteoporosis reports.

[13]  O. Johnell,et al.  Mortality after osteoporotic fractures , 2004, Osteoporosis International.

[14]  George C Babis,et al.  Factors affecting the risk of hip fractures. , 2007, Injury.

[15]  J. Zuckerman,et al.  Hip fracture epidemiology: a review. , 1999, American journal of orthopedics.

[16]  Maysam F. Abbod,et al.  Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction , 2012, Adv. Fuzzy Syst..

[17]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[18]  T. M. Kashner,et al.  Survival experience of aged hip fracture patients. , 1989, American journal of public health.

[19]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[21]  Kenneth J Ottenbacher,et al.  Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture. , 2004, Annals of epidemiology.

[22]  W. Baxt Application of artificial neural networks to clinical medicine , 1995, The Lancet.

[23]  Eugene Lin,et al.  An artificial neural network approach to the drug efficacy of interferon treatments. , 2006, Pharmacogenomics.

[24]  A M Jette,et al.  Functional recovery after hip fracture. , 1987, Archives of physical medicine and rehabilitation.

[25]  J. Shieh,et al.  Ensembled artificial neural networks to predict the fitness score for body composition analysis , 2011, The journal of nutrition, health & aging.

[26]  Martha Pulido,et al.  Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange , 2014, Inf. Sci..

[27]  Lutz Prechelt,et al.  Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.

[28]  T. Tai,et al.  Risk factors for hip fracture in older adults: a case–control study in Taiwan , 2010, Osteoporosis International.

[29]  Maysam F. Abbod,et al.  Brain death prediction based on ensembled artificial neural networks in neurosurgical intensive care unit , 2011 .

[30]  K J Ottenbacher,et al.  Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke. , 2001, Journal of clinical epidemiology.

[31]  Enzo Grossi,et al.  Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database , 2011, PloS one.

[32]  Jiann-Shing Shieh,et al.  Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study , 2013, BMC Musculoskeletal Disorders.

[33]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Armando Freitas da Rocha,et al.  Neural Nets , 1992, Lecture Notes in Computer Science.

[35]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[36]  Jenq-Neng Hwang,et al.  Neural networks for intelligent multimedia processing , 1998 .