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

fYear

2012

fDate

10-12 Sept. 2012

Firstpage

1

Lastpage

5

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on

Conference_Location

Langkawi

Print_ISBN

978-1-4673-1581-4

Type

conf

DOI

10.1109/ICSSBE.2012.6396583

Filename

6396583