Table of Contents
Advances in Artificial Intelligence
Volume 2015 (2015), Article ID 270165, 7 pages
http://dx.doi.org/10.1155/2015/270165
Research Article

Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

1Department of Mathematics and Statistics, University of South Florida, 4202 E. Fowler Avenue, CMC 342, Tampa, FL 33620, USA
2Department of Mathematics and Statistics, University of South Florida, 4202 E. Fowler Avenue, CMC 366, Tampa, FL 33620, USA

Received 17 September 2014; Revised 17 February 2015; Accepted 23 February 2015

Academic Editor: Jun He

Copyright © 2015 Taysseer Sharaf and Chris P. Tsokos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.