From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

arXiv:2608.05203 · cs.AI, cs.LG · Submitted 2026-08-05 · Read on arXiv

Esra Zihni, Katryna Cisek, Hamzah Ziadeh, Hendrik Knoche, Robert Mikulik, John D. Kelleher

cs.AI, cs.LG

Submitted: 2026-08-05

Comments: 9 pages, 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• AUROC — 5× in this paper · explained in 3 episodes
• Feature Importance — 5× in this paper · explained in 2 episodes
• Hyperparameters — 4× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Feature Engineering — 2× in this paper · explained in 3 episodes
• Overfitting — 2× in this paper · explained in 2 episodes
• Actionability — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 4 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• SHAP Values — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• CatBoost: gradient boosting with categorical features support

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