Nonlinear Survival Regression Using Artificial Neural Network
作者： Akbar Biglarian , Enayatollah Bakhshi , Ahmad Reza Baghestani , Mahmood Reza Gohari , Mehdi Rahgozar
DOI: 10.1155/2013/753930
关键词: Model building 、 Event (probability theory) 、 Computer science 、 sort 、 Artificial neural network 、 Proportional hazards model 、 Regression 、 Statistics 、 Nonlinear system 、 Censoring (statistics)
摘要: Survival analysis methods deal with a type of data, which is waiting time till occurrence an event. One common method to analyze this sort data Cox regression. Sometimes, the underlying assumptions model are not true, such as nonproportionality for model. In building, choosing appropriate depends on complexity and characteristics that effect appropriateness strategy, used nowadays frequently, artificial neural network (ANN) needs minimal assumption. This study aimed compare predictions ANN models by simulated sets, average censoring rate were considered 20% 80% in both simple complex All simulations comparisons performed R 2.14.1.
hindawi.com参考文章(27)
W. Nick Street, William H. Wolberg, Chih Lin Chi, Application of Artificial Neural Network-Based Survival Analysis on Two Breast Cancer Datasets american medical informatics association annual symposium. ,vol. 2007, pp. 130- 134 ,(2007)
Brian D. Ripley, Ruth M. Ripley, Neural networks as statistical methods in survival analysis Clinical Applications of Artificial Neural Networks. pp. 237- 255 ,(2001) , 10.1017/CBO9780511543494.011
Rachel Bittern, Peter Moore, Robin Marshall, Robert Steele, Sergey Dolgobrodov, Alfred Cuschieri, An artificial neural network for analysing the survival of patients with colorectal cancer. the european symposium on artificial neural networks. pp. 103- 108 ,(2005)
Christopher M. Bishop, Neural networks for pattern recognition ,(1995)
Ernest Preston Goss, George S. Vozikis, Improving health care organizational management through neural network learning. Health Care Management Science. ,vol. 5, pp. 221- 227 ,(2002) , 10.1023/A:1019760901191
Bert A. Mobley, Eliot Schechter, William E. Moore, Patrick A. McKee, June E. Eichner, Predictions of coronary artery stenosis by artificial neural network. Artificial Intelligence in Medicine. ,vol. 18, pp. 187- 203 ,(2000) , 10.1016/S0933-3657(99)00040-8
Federico Ambrogi, Nicola Lama, Patrizia Boracchi, Elia Biganzoli, Selection of artificial neural network models for survival analysis with Genetic Algorithms Computational Statistics & Data Analysis. ,vol. 52, pp. 30- 42 ,(2007) , 10.1016/J.CSDA.2007.05.001
Elia Biganzoli, Patrizia Boracchi, Ettore Marubini, A general framework for neural network models on censored survival data Neural Networks. ,vol. 15, pp. 209- 218 ,(2002) , 10.1016/S0893-6080(01)00131-9
Ruth M. Ripley, Adrian L. Harris, Lionel Tarassenko, Non-linear survival analysis using neural networks Statistics in Medicine. ,vol. 23, pp. 825- 842 ,(2004) , 10.1002/SIM.1655
Antonio Eleuteri, Roberto Tagliaferri, Leopoldo Milano, Sabino De Placido, Michele De Laurentiis, A novel neural network-based survival analysis model international joint conference on neural network. ,vol. 16, pp. 855- 864 ,(2003) , 10.1016/S0893-6080(03)00098-4