Variable Selection in Weibull Accelerated Survival Model Based on lemur’s optimization algorithm
DOI:
https://doi.org/10.62933/0rq0gs73Keywords:
Weibull Distribution , High Dimensional , Accelerated Failure Time , Feature Selection , Lemur’s AlgorithmAbstract
Survival models play a key role in analyzing time-to-event data. Accelerated time models are useful when the effect of the covariates on the survival time is proportional throughout the follow-up period. The well-known Weibull accelerated failure-time model (AFT), as an accelerated time model, is widely employed in survival analyses. The high-dimensional variables picked, considering most of the low-dimensional AFT model-based variable selectors, assume that the covariate effects are constant throughout the period. However, the Weibull AFT model includes time transformations enabling constant, increasing, or decreasing impacts during the entire study, and thus, it is a more flexible approach. Lemurs’ optimization algorithm (LOA), a new powerful algorithm, is employed in the covariate selection. It is important to select appropriate significant covariates for AFT models in practice. However, like all other high-dimensional data, the lemur faces the curse of dimensionality problem in Weibull AFT model-based variable selection. Thus, it is necessary to have a purposeful variable selection algorithm for Weibull AFT models that considers the increasing, constant, or decreasing effects of covariates.
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Copyright (c) 2025 Ahmed Naziyah Abdullah, Qutaiba Nabil Nayef AL-Qazaz (Author)
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