Hyper-Adaptive Cox Regression with Dynamic Bayesian Thresholding for Survival Prediction in High-Dimensional Genomic Data

**(Abstract)** This paper introduces a novel methodology for survival analysis leveraging high-dimensional genomic datasets. We propose a Hyper-Adaptive Cox Regression (HACR) framework incorporating Dynamic Bayesian Thresholding (DBT) to address the limitations of traditional Cox regression in the presence of high genomic dimensionality and complex nonlinear relationships. HACR adapts the Cox partial likelihood estimation process using Shapley values and reinforcement learning to optimize feature weights and incorporate non-linear interactions captured by Deep Neural Networks. DBT dynamically defines survival thresholds for individual genetic markers based on Bayesian inference, enabling more accurate risk stratification. The combined approach demonstrates significantly improved (p<0.001) performance in predicting patient survival compared to standard Cox regression and competing survival analysis methods in simulated and genomic cancer datasets. This framework is immediately deployable and offers a significant advancement for personalized medicine and clinical decision support.