Title :
On the use of artificial neural networks for the analysis of survival data
Author :
Brown, Stephen F. ; Branford, Alan J. ; Moran, William
Author_Institution :
Dept. of Math. & Stat., Flinders Univ. of South Australia, Bedford Park, SA, Australia
fDate :
9/1/1997 12:00:00 AM
Abstract :
Artificial neural networks are a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the measured inputs and the quantity to be predicted. We show that the results obtained when neural networks are applied to survival data depend critically on the treatment of censoring in the data. When the censoring is modeled correctly, neural networks are a robust model independent technique for the analysis of very large sets of survival data
Keywords :
backpropagation; design of experiments; estimation theory; failure analysis; neural nets; probability; statistical analysis; backpropagation; failure time; neural networks; statistical analysis; survival data analysis; Artificial neural networks; Data analysis; Diseases; Independent component analysis; Neural networks; Particle measurements; Robustness; Statistical analysis; Statistics; Time measurement;
Journal_Title :
Neural Networks, IEEE Transactions on
