A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making

• URL: http://arxiv.org/abs/2309.11101v1
• Date: Wed, 20 Sep 2023 07:15:48 GMT
• Title: A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
• Authors: Adrien Benamira, Tristan Guerand, Thomas Peyrin
• Abstract summary: In this study, we introduce a neural network framework, $textitTruth Table rules$ (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 $textitTruth Table nets$ (TTnet), a family of deep neural networks initially developed for formal verification.
• Score: 5.666761232081187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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