Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data. (15th March 2015)

• Record Type: Journal Article
• Authors: Sharaf, Taysseer; Tsokos, Chris P.
• Other Names: He Jun Academic Editor.
• Is Part Of: Advances in artificial intelligence. Volume 2015(2015)
• Publication Date: 2015-03-15
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1155/2015/270165
• Languages: English
• ISSNs: 1687-7470

Abstract

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.