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

Chuhan Zhang

Journal of Sustainability, Policy, and Practice, 2025, vol. 1, issue 2, 57-68

Abstract: 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.

Keywords: cancer survival prediction; multi-dimensional feature selection; machine learning optimization; clinical decision support (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:1:y:2025:i:2:p:57-68

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