Abstract
How structured constraints can keep a clinical-risk model inspectable, plus the validation still needed before deployment.
Problem framing
The useful question is not which technique sounds most advanced, but which assumptions fit the data-generating process and the cost of failure. Specify the unit of generalization, prevent leakage, and decide what evidence would change the conclusion.
Method lens
Interpretability is defined for a user and a decision. Intrinsic structure can make a model easier to audit; post-hoc explanations remain approximations whose stability and fidelity must be tested.
Evaluation before conclusion
- Split at the level of the intended generalization claim.
- Report variation across repetitions or folds when available.
- Compare relevant baselines under the same protocol.
- Separate retrospective discrimination from operational utility.
- Record preprocessing, configuration, and expected outputs.
Limitations
This is a research-communication note, not clinical, diagnostic, or investment guidance. It condenses method choices and does not replace the underlying paper, dataset documentation, or domain-expert review.
Related paper
Interpretable heart disease risk prediction via FCA-constrained logistic regression
References
Content and links last reviewed 2026-07-26.