Clinical Raman Diagnosis for Healthcare Optimization Using Reinforcement Learning and Diffusion Models

High-dimensional medical data drive clinical decisions, and Raman spectroscopy is a key modality. However, real-world data often show nonstationary distributions, peak overlap, and batch effects. Fixed policies and conservative sampling increase the risk of overfitting. We therefore need a clinical framework that can reconstruct policies and enable safe exploration. Previous methods rely on full-spectrum modeling or static feature subsets, which do not support rapid policy switching during screening or stable detection of weak peaks. The core challenges are poor policy reconstruction across scenarios and limited exploration diversity. To this end, we propose the DecisionEC framework. Our idea uses structured decision making from reinforcement learning to lock onto clinically relevant structures and a diffusion prior to maintain high-quality global search. Specifically, we present Structure-Aware Option-Q (SAOQ), which abstracts stable peak clusters as reusable options and learns budget-aware high-level actions to meet sensitivity and specificity targets. Moreover, we introduce Diffusion-Guided Generational Competition (DGGC), which uses a conditional diffusion prior to reshape the candidate distribution and employs the Diffusion Slime Mould Algorithm (DSMA) to retrieve valuable peak-cluster combinations efficiently. Extensive experiments on a hospital-derived gastric cancer Raman spectroscopy classification task and multiple high-dimensional biomedical datasets validate the effectiveness of the proposed method.