Variable Selection in Gompertz Parametric Survival Regression Model

Abstract

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Gompertz  parametric survival regression model is the most popular model in regression analysis for censored survival data. In this paper, an invasive weed optimization (IWO) as an evolutionary algorithm is employed in Gompertz proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy. Experimental results show that the IWO significantly outperforms two competitor methods, AIC and BIC in terms of the area under the curve and the number of the selected genes.