Title :
Feed forward neural networks models for survival analysis
Author :
Dezfouli, Hamid Nilsaz ; Bakar, Mohd Rizam Abu
Author_Institution :
Inst. for Math. Res., UPM, Serdang, Malaysia
Abstract :
Artificial neural networks are increasingly being considered as an addition to the classical and modern statistical methods and have been found applications in a wide variety of medical problems. Due to their less restrictive framework that can incorporate nonlinearity and covariate interactions they have been proposed for the statistical analysis of censored survival data. This study presents different neural network strategies which have been suggested for modeling survival data.
Keywords :
covariance analysis; data analysis; feedforward neural nets; medical administrative data processing; medical computing; artificial neural networks; censored survival data analysis; covariate interactions; feed forward neural networks models; medical problems; nonlinearity; statistical analysis; statistical methods; survival data modeling; Artificial neural networks; Biological neural networks; Breast cancer; Data models; Hazards; Logistics; Artificial neural networks; censored data; cox proportional hazard; survival analysis; survival probability;
Conference_Titel :
Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1581-4
DOI :
10.1109/ICSSBE.2012.6396583
