Artificial Neural Network Model for Predicting Lung Cancer Survival

Abstract

The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.