LEAF: benchmarking multiobjective evolutionary feature selection over large language model–extracted symptoms for interpretable Traditional Chinese Medicine respiratory syndrome differentiation

Differentiating Traditional Chinese Medicine (TCM) respiratory syndromes requires weighing overlapping symptoms together with tongue and pulse findings, a task that most AI systems for TCM perform end-to-end but without an interpretable intermediate representation. In this study, we addressed this gap with LEAF (LLM extraction and multiobjective feature selection), a two-stage modular framework evaluated on 3,253 records covering 14 respiratory syndromes from the TCM-SD benchmark. Its first large language model (LLM) stage extracted a 35-item symptom ontology that was built entirely from public TCM sources; among three interchangeable LLMs, Qwen2.5-14B-Instruct was ranked as the best, followed by Phi-4 and the TCM domain model BianCang. Its second evolutionary-computation stage benchmarked four multiobjective evolutionary algorithms [non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm 2 (SPEA2), competitive mechanism-based multiobjective particle swarm optimizer (CMOPSO), and non-dominated sorting differential evolution (NSDE)] over 10 seeds, and they were compared using Friedman’s and Nemenyi’s testing in terms of both classification outcome and search-process quality. The four algorithms showed no significant separation on held-out macro-F1, although NSDE led nominally, and none exceeded the full-feature baseline on point estimates. In contrast, hypervolume differed significantly and favored NSGA-II, the weakest of the four on held-out macro-F1. A four-classifier panel found no algorithm to be significantly different from either the full-feature baseline or a random-feature control, which was significantly outperformed only by the full-feature baseline. Applying NSDE to the remaining two LLM pipelines reproduced the original ranking, and its representative solution was fully attested against the knowledge base of TCM-SD, although not typically so across all the 10 seeds. These results show that an LLM-extraction frontend built exclusively from public knowledge can provide a representation over which evolutionary feature selection cuts the candidate feature set roughly in half without a statistically detectable cost in performance, albeit at a small point-estimate cost; that hypervolume and inverted generational distance are not reliable stand-ins for classification-relevant algorithm selection here; and that a no-gold-standard proxy evaluation can substitute human annotation when expert time is limited. Because both stages plug in and out freely, the framework can absorb stronger components in either stage as they become available.