Application of Artificial Neural Network-Based Survival Analysis on Two Breast Cancer Datasets
作者： W. Nick Street , William H. Wolberg , Chih Lin Chi
DOI:
关键词:
摘要: This paper applies artificial neural networks (ANNs) to the survival analysis problem. Because ANNs can easily consider variable interactions and create a non-linear prediction model, they offer more flexible of time than traditional methods. study compares ANN results on two different breast cancer datasets, both which use nuclear morphometric features. The show that successfully predict recurrence probability separate patients with good (more five years) bad (less prognoses. Results are not as clear when separation is done within subgroups such lymph node positive or negative.
researchgate.net 参考文章(14)
W. Nick Street, A Neural Network Model for Prognostic Prediction international conference on machine learning. pp. 540- 546 ,(1998)
M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan, J. A. Olson, J. R. Marks, J. R. Nevins, Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences of the United States of America. ,vol. 98, pp. 11462- 11467 ,(2001) , 10.1073/PNAS.201162998
Elisa T. Lee, Statistical Methods for Survival Data Analysis ,(1980)
P. S. Saggu, E. T. Lee, Statistical Methods for Survival Data Analysis. The Statistician. ,vol. 43, pp. 607- 608 ,(1994) , 10.2307/2348154
David R. Cox, Regression Models and Life-Tables Springer Series in Statistics. ,vol. 34, pp. 527- 541 ,(1992) , 10.1007/978-1-4612-4380-9_37
Michele De Laurentiis, Peter M. Ravdin, A technique for using neural network analysis to perform survival analysis of censored data. Cancer Letters. ,vol. 77, pp. 127- 138 ,(1994) , 10.1016/0304-3835(94)90095-7
E. L. Kaplan, Paul Meier, Nonparametric Estimation from Incomplete Observations Springer Series in Statistics. ,vol. 53, pp. 319- 337 ,(1992) , 10.1007/978-1-4612-4380-9_25
William H. Wolberg, W.Nick Street, O.L. Mangasarian, Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates Cancer Letters. ,vol. 77, pp. 163- 171 ,(1994) , 10.1016/0304-3835(94)90099-X
Michele De Laurentiis, Peter M. Ravdin, Survival analysis of censored data: neural network analysis detection of complex interactions between variables. Breast Cancer Research and Treatment. ,vol. 32, pp. 113- 118 ,(1994) , 10.1007/BF00666212
Olvi L. Mangasarian, W. Nick Street, William H. Wolberg, Breast Cancer Diagnosis and Prognosis Via Linear Programming Operations Research. ,vol. 43, pp. 570- 577 ,(1995) , 10.1287/OPRE.43.4.570