A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
This paper introduces a statistically grounded framework using Bernoulli Naïve Bayes (BNB) for interpretable rule-based clinical classification. It applies supervised χ²-guided binarization to continuous variables, achieving AUC scores of 0.800 on Pima Indians Diabetes, 0.984 on Wisconsin Breast Cancer, and 0.919 on Heart Failure Prediction. The framework provides transparent decision rules and calibrated risk estimates, with model inference reproducible using only a reference table and basic arithmetic.
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[Submitted on 16 Jul 2026]
Title:A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
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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 Naïve Bayes (BNB) model. The method applies supervised $\chi^2$-guided statistical binarization to continuous variables, identifying thresholds that maximize association with clinical outcomes within the training data. This transformation allows BNB to operate effectively on continuous medical data without sacrificing its inherent transparency. The approach was evaluated on three benchmark datasets, Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction, achieving area-under-the-curve (AUC) scores of 0.800 for the Pima analysis, 0.984 for Wisconsin Breast Cancer, and 0.919 for Heart Failure Prediction. In addition to discrimination, probabilistic reliability was assessed using leakage-safe cross-validated calibration analysis including Brier score, calibration intercept/slope, and post-hoc beta calibration, which improved probability calibration across datasets. These results suggest that a statistically interpretable framework can achieve performance comparable to more complex models while providing explicit, clinically meaningful decision rules and calibrated risk estimates. To illustrate this transparency concretely, a complete worked example demonstrates that model inference can be reproduced using only a reference table and basic arithmetic, without access to software or proprietary tools. This work offers a practical approach to supporting trustworthy and generalizable AI in real-world healthcare settings.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.15394 [cs.LG]
(or arXiv:2607.15394v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.15394
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinming Huang [view email] [v1] Thu, 16 Jul 2026 18:48:19 UTC (390 KB)
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