A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
Machine Learning
Summary
The authors created a method that turns complex medical data into simple yes/no questions to help predict health outcomes in a clear way. They use a special statistical technique to find the best cut-off points in continuous data, making a rule-based model that doctors can understand easily. Their approach works well on three medical datasets, showing good accuracy and reliable risk predictions. They also provide a clear example showing that anyone can follow the decision process using just a table and basic math, without needing complicated software. This method helps build trustworthy AI tools that are both accurate and easy for clinicians to interpret.
Authors
Antony Garcia, Adrian Noriega, Gabrielle Britton, Xinming Huang
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.