Evolutionary Large Language Models Driven Cancer Cell Data Analysis

Cancer diagnosis and treatment planning face significant challenges due to the rapid growth of cancer cell data and the increasing complexity of analysis. While large language models (LLMs) show promise for medical data analysis, their deployment in distributed healthcare environments encounters efficiency and accuracy challenges as data volumes and model complexity increase. LLM refers to a large-parameter transformer network rather than a natural-language generation system: the model takes structured cancer cell features and, for one of the two datasets, histology images as input, and returns a diagnostic classification rather than free text. This paper introduces EvoHealth-LLM, a novel method optimizing LLM performance in cancer cell analysis using differential evolution principles. Our approach employs differential evolution to guide model parameter optimization through intelligent mutation and crossover operations for enhanced cancer feature detection. The method incorporates three key innovations: a differential evolution-based optimization strategy enhanced by a cancer feature attention mechanism improving diagnostic accuracy, a health-adaptive data allocation algorithm efficiently distributing cancer cell data across medical nodes based on processing capabilities, and a self-adaptive control mechanism for evolution parameters ensuring optimal model convergence across diverse cancer types. Experimental results on the cancer genome atlas (TCGA) and multi-cancer datasets demonstrate EvoHealth-LLM's superior performance, achieving 80% classification accuracy on TCGA data in 4.09 hours—outperforming the baseline methods evaluated in this study. The system maintains 75% resource utilization efficiency across distributed nodes while ensuring consistent model quality, addressing critical challenges in computational cancer analysis.