A New Interpretable Neural Network-Based Rule Model for Healthcare
  Decision Making
Adrien Benamira Tristan Guerand Thomas Peyrin
In healthcare applications, understanding how machine/deep learning models
make decisions is crucial. In this study, we introduce a neural network
framework, $\textit{Truth Table rules}$ (TT-rules), that combines the global
and exact interpretability properties of rule-based models with the high
performance of deep neural networks. TT-rules is built upon $\textit{Truth
Table nets}$ (TTnet), a family of deep neural networks initially developed for
formal verification. By extracting the necessary and sufficient rules
$\mathcal{R}$ from the trained TTnet model (global interpretability) to yield
the same output as the TTnet (exact interpretability), TT-rules effectively
transforms the neural network into a rule-based model. This rule-based model
supports binary classification, multi-label classification, and regression
tasks for small to large tabular datasets. After outlining the framework, we
evaluate TT-rules' performance on healthcare applications and compare it to
state-of-the-art rule-based methods. Our results demonstrate that TT-rules
achieves equal or higher performance compared to other interpretable methods.
Notably, TT-rules presents the first accurate rule-based model capable of
fitting large tabular datasets, including two real-life DNA datasets with over
20K features.