  This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method,
which is among the first works that melds large language models (LLMs) with
evolutionary algorithms for feature selection (FS) tasks and demonstrates its
effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH
harnesses the crossover and mutation capabilities inherent in LLMs within an
evolutionary framework, significantly improving FS through the model's
comprehensive world knowledge and its adaptability to a variety of roles. Our
evaluation of this methodology spans three crucial MPA tasks: stroke,
cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional
FS methods in pinpointing essential features for medical applications.
ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction
and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in
cardiovascular disease prediction. The study emphasizes the critical role of
incorporating domain-specific insights, illustrating ICE-SEARCH's robustness,
generalizability, and convergence. This opens avenues for further research into
comprehensive and intricate FS landscapes, marking a significant stride in the
application of artificial intelligence in medical predictive analytics.
