Stability-Bound Binary Rule Search: A General Workflow for Explainable Prediction on Binarized Clinical Data

Explainable prediction is increasingly required in clinical decision support, especially when models must generalize across institutions. We present a stability-bound binary rule search workflow that operates on fully binarized clinical data and expresses decisions as sparse, human-readable rules. Clinical variables are converted into 0/1 indicators using clinically meaningful thresholds, so that each rule corresponds to a binary mask over a small set of interpretable features. A Binary Rule Search (BRS) engine explores conjunctions of up to four predictors (k=1–4), and candidate rules are evaluated by the Matthews-correlation-coefficient (MCC) on development and validation splits. Robustness is summarized by the Stability-Bound-Rule-Score (SBRS), a geometric-style combination of the lower 95% confidence bounds of MCC in both splits. The workflow was applied to two open-access datasets: a heart attack dataset (303 patients) and a hepatitis C dataset (615 patients). In the heart attack data, a four-feature rule combining age 55–64 years, typical chest pain, absence of angiographically stenosed vessels (CA = 0) and a reversible thallium perfusion defect achieved MCC 0.71 and 0.73 in the development and validation sets, with SBRS = 1.59. In the hepatitis C data, rules built from elevated aspartate aminotransferase together with intermediate or high alkaline phosphatase and increased bilirubin reached MCC 0.75 and 0.84, with SBRS = 1.67. Because all predictors are binarized, the final rules can be displayed as compact binary mask plots or implemented as short checklists and look-up tables. Overall, this stability-bound binary rule search workflow yields sparse, stable and clinically interpretable rule sets for cardiovascular risk stratification and chronic liver disease screening. © 2026, Iğdır University. All rights reserved.