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

Taysseer Sharaf,Chris P Tsokos

doi:10.1155/2015/270165

Taysseer Sharaf, Chris P Tsokos

Open Access

https://doi.org/10.1155/2015/270165

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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.

Highlights

Artificial neural network (ANN) is becoming one of the most popular alternatives to conventional statistical modeling
Baesens et al compared the ANN model used with other survival analysis models like logistic regression and Cox PH and the results came in favor of the ANN
We used ten time intervals (12 months each) and in order to do the comparison between the Partial logistic artificial neural network (PLANN) and Mani’s method we used 10 inputs for the ten time intervals in PLANN instead of one so that we can compare the output of the PLANN with the second method

Summary

Introduction

Artificial neural network (ANN) is becoming one of the most popular alternatives to conventional statistical modeling. It is conceived as an advanced generalized linear model. We have seen various applications of ANN utilized in different scientific subjects like engineering, economics, environment, and health, among others. Van Hinsbergen et al 2009 [1] applied artificial neural networks to short-term time prediction of traffic travel time. Baesens et al 2005 [3] used ANN to predict survival time of personal loan data.

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