TL;DR for operators Five minutes of on-chain trading activity contain enough signal to support one-hour rug-pull screening on Solana, but the signal is not reliably portable across venues. A model that performs reasonably on PumpFun can lose much of its separation when moved to Raydium, and vice versa. Training on both venues helps—especially for XGBoost and Random Forest—but does not remove the platform effect. ...
TL;DR for operators CW-B is not interesting because it discovers a novel model architecture. It is interesting because it treats three routinely neglected design decisions as one system: Rare classes receive more influence during training. Class weights are computed separately inside each training fold, preventing common phenotypes from dominating the tree-building process. Missing values retain their provenance. The pipeline imputes a numerical value but also adds an indicator showing that the original measurement was absent. Clinical priorities are audited separately from aggregate performance. Stable coronary artery disease, acute coronary syndrome, and non-obstructive coronary disease are grouped into a predefined evaluation set because missing them can alter follow-up and treatment pathways. On a five-class dataset containing 4,354 patient records and 57 structured features, CW-B records the strongest accuracy, Macro-F1, balanced accuracy, and prioritized F1 among the tested tree, ensemble, and neural baselines. Its balanced accuracy reaches 0.73, compared with 0.66 for a larger XGBoost baseline. Its prioritized F1 is 0.69, compared with 0.67 for that baseline. ...