Optimization of Cancer Patient Survival Prediction Algorithms Based on Multi-Dimensional Feature Selection

Cancer survival prediction remains a critical challenge in oncology; this challenge requires sophisticated computational approaches to handle complex clinical data patterns. This study presents an optimized algorithmic framework that integrates multi-dimensional feature selection techniques with advanced survival prediction models to enhance prognostic accuracy in cancer patients. The proposed methodology combines clinical, genomic, and imaging features through a hierarchical selection process, enabling more precise survival time estimation. Experimental validation using a comprehensive cancer dataset demonstrates significant improvements in pre-diction performance, achieving C-index values of 0.847 and accuracy rates exceeding 89% across multiple cancer types. The multi-dimensional approach successfully identifies critical prognostic biomarkers while reducing computational complexity through intelligent feature reduction strategies. Clinical validation confirms the practical applicability of the optimized algorithms in real-world oncology settings, providing oncologists with enhanced decision-support capabilities for patient care planning and treatment protocol selection.