01. A Classification Technique for Survival Data using Bayesian AFT Model with Frailty Effect

We provide a proposed Bayesian classifier to classify cancer biomarkers. Before classification, biomarkers or efficient markers from high-dimensional survival data need to be identified. Currently, it is an emerging area in oncology. A three-stepped feature selection method is also introduced to select the most efficient markers from microarray data. AFT model with the frailty effect is used in the classification and analysis of the data in the Bayesian framework. The cutoff value for each selected gene expression has been obtained through classification using the minimum deviance criterion in the AFT model with the frailty effect. The frailty effect is considered for dealing with unobserved heterogeneity present in the expression value of the subject for investigating the risk effects on the cancer dataset. A simulation study is also done to verify the methodology's validation. The brier score is obtained to know the effectiveness of the proposed classification procedure. The proposed classification method demonstrates its efficacy in gauging the risk impact on diverse patients by utilizing biomarkers, enabling swift estimation and prompt action for disease treatment. This approach finds practical application through the analysis of two high-dimensional, real-world lung cancer datasets, offering valuable insights for effective healthcare interventions.

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Women in Statistics and Data Science 2024