A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data

arXiv:2607.15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility. We introduce a statistically grounded framework that provides fully interpretable, rule-based clinical classification using the Bernoulli Na\"ive Bayes (BNB) model. The method applies supervised $\chi^2$-guided statistical binarization to continuous variables, identifying thresholds that maximize association with cli...