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

2026-07-16

AI Summary

This study addresses the clinical deployment challenges of black-box models in medical AI, which suffer from poor interpretability and reproducibility. To overcome these limitations, the authors propose a fully transparent and manually reproducible classification framework based on Bernoulli Naïve Bayes. The approach employs chi-squared test–guided supervised binarization to convert continuous clinical variables into threshold-based rules with clear clinical meaning. Probability calibration is performed using leakage-proof cross-validation, incorporating Brier score, calibration intercept and slope, and Beta posterior calibration. Evaluated on three medical datasets, the model achieves AUCs ranging from 0.800 to 0.984 while substantially improving calibration quality, enabling high-performance predictions that can be executed without software dependencies through manual inference.

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 $χ^2$-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.

Problem

• interpretability
• medical data classification
• black-box models
• clinical decision rules
• reproducibility

Innovation

• interpretable AI
• threshold-based binarization
• Bernoulli Naïve Bayes
• calibrated risk estimation
• χ²-guided feature discretization