Fixing a Model That Learned Worse Cancer Means Lower Risk: Monotonic Constraints in Bladder Cancer Recurrence Prediction

In a UK multicentre trial, an unconstrained XGBoost model incorrectly learned that higher tumour stage and carcinoma in situ predicted lower bladder cancer recurrence risk, a finding that conventional metrics such as discrimination, calibration, and SHAP failed to detect. The authors introduced a counterfactual direction test and a monotonic‑constraint framework, which removed the inversion without harming model performance and even outperformed established risk systems. The study demonstrates that such tests should be routine before deploying predictive models in clinical settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.