Variable Selection in Weibull Accelerated Survival Model Based on Chaotic Sand Cat Swarm Algorithm

Abstract

In medical research, proportional hazard models are much more common, but accelerated failure time (AFT) models are still widely used. Variables in the AFT model influence the event time by altering the logarithm of the dependent variable's survival time. The parametric forms typically utilized by AFT models are restricted and cannot be represented otherwise. The selection of variables and parameter estimation for the Weibull distribution is a common practice. This predictive approach is often applied in reliability studies in engineering and medical forecasts, particularly for estimating survival time. Additionally, we present an empirical example using our prediction method on a publicly accessible dataset. Sand cat swarm optimization (SCSO) is a new metaheuristic algorithm that mimics the survival behavior of sand cats. The results reveal that SCSO outperforms other methods in terms of convergence speed and finds all or most local/global optima. The SCSO algorithm is introduced to identify critical variables in the Weibull AFT regression model. Thus, variations of the SCSO algorithm can be applied to address the Weibull AFT problem.