CACTUS Improves Feature Stability Under Missingness
Paulina Tworek et al. (submitted 19 Feb 2026) present CACTUS, an explainable machine learning framework addressing robustness, interpretability, and feature instability in small, incomplete clinical datasets. They evaluate CACTUS on a 568-patient haematuria cohort for bladder cancer, benchmarking against random forests and gradient boosting with controlled missingness. CACTUS matches or exceeds predictive performance while preserving markedly higher top-feature stability as data completeness degrades, aiding trustworthy decision support.
Scoring Rationale
Novel, actionable framework with strong relevance; limited by single-cohort arXiv preprint evaluation pending peer review.
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