LEVERAGING DEEP LEARNING IN SURVIVAL ANALYSIS FOR ENHANCED TIME-TO-EVENT PREDICTION

Survival analysis is a critical statistical approach for modeling time-to-event outcomes across disciplines such as healthcare, engineering, and social sciences. Traditional methods, including Cox proportional hazards models, often struggle with complex, high-dimensional data and non-linear relationships. Recent advancements in deep learning have led to innovative models that significantly enhance survival prediction by capturing intricate patterns and dependencies in time-to-event data. This study explores state-of-the-art deep learning frameworks—such as DeepSurv, DeepHit, and recurrent neural networks—for survival analysis, emphasizing their architecture, performance, and application across diverse datasets. The integration of deep learning enables more accurate risk estimation and personalized prognostics, revolutionizing predictive modeling in survival data contexts. We also discuss challenges related to interpretability, data censoring, and model evaluation, proposing future research directions for robust and explainable deep survival models.