Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

Nascimben, Mauro; Venturin, Manolo; Rimondini, Lia

doi:10.2478/caim-2021-0003

Abstract

Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.

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Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

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