# Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

arXiv: https://arxiv.org/abs/1809.04547 · HTML: https://mlsanity.com/paper/1809.04547/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 24 of 24)

- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)
- [Coalesced Multi-Output Tsetlin Machines with Clause Sharing](https://mlsanity.com/paper/2108.07594/) (2021)
- [On the Convergence of Tsetlin Machines for the XOR Operator](https://mlsanity.com/paper/2101.02547/) (2021)
- [Enhancing Interpretable Clauses Semantically using Pretrained Word Representation](https://mlsanity.com/paper/2104.06901/) (2021)
- [Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines](https://mlsanity.com/paper/2105.04708/) (2021)
- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine](https://mlsanity.com/paper/2105.14506/) (2021)
- [An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites](https://mlsanity.com/paper/2406.00704/) (2024)
- [Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines](https://mlsanity.com/paper/2406.02648/) (2024)
- [Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings](https://mlsanity.com/paper/2501.18998/) (2025)
- [Exploring State Space and Reasoning by Elimination in Tsetlin Machines](https://mlsanity.com/paper/2407.09162/) (2024)
- [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024)
- [Self-timed Reinforcement Learning using Tsetlin Machine](https://mlsanity.com/paper/2109.00846/) (2021)
- [Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses](https://mlsanity.com/paper/2501.19018/) (2025)
- [On the Equivalence of the Weighted Tsetlin Machine and the Perceptron](https://mlsanity.com/paper/2212.13634/) (2022)
- [The Sparse Tsetlin Machine: Sparse Representation with Active Literals](https://mlsanity.com/paper/2405.02375/) (2024)
- [Verifying Properties of Tsetlin Machines](https://mlsanity.com/paper/2303.14464/) (2023)
- [Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection](https://mlsanity.com/paper/2305.10219/) (2023)
- [Efficient Data Fusion using the Tsetlin Machine](https://mlsanity.com/paper/2310.17207/) (2023)
- [AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines](https://mlsanity.com/paper/2507.19803/) (2025)
- [A Comparative Study of Feature Selection in Tsetlin Machines](https://mlsanity.com/paper/2508.06991/) (2025)
- [A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine](https://mlsanity.com/paper/2510.01906/) (2025)
- [LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines](https://mlsanity.com/paper/2604.12223/) (2026)
- [Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability](https://mlsanity.com/paper/2606.19815/) (2026)

## Abstract (arXiv)

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled text, utilizing conjunctive clauses to represent the particular facets of each category. Indeed, even the absence of terms (negated features) can be used for categorization purposes. Our empirical comparison with Na"ive Bayes, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, is quite conclusive. The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset. On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets. Finally, our GPU implementation of the Tsetlin Machine executes 5 to 15 times faster than the CPU implementation, depending on the dataset. We thus believe that our novel approach can have a significant impact on a wide range of text analysis applications, forming a promising starting point for deeper natural language understanding with the Tsetlin Machine.