# AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines

arXiv: https://arxiv.org/abs/2507.19803 · HTML: https://mlsanity.com/paper/2507.19803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications](https://mlsanity.com/paper/1809.04547/) (2018)
- [Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification](https://mlsanity.com/paper/2301.10181/) (2023)
- [Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction](https://mlsanity.com/paper/2505.00171/) (2025)

## Abstract (arXiv)

Bladder cancer claims one life every 3 minutes worldwide. Most patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC), yet up to 70% recur after treatment, triggering a relentless cycle of surgeries, monitoring, and risk of progression. Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases. We propose an interpretable AI model using the Tsetlin Machine (TM), a symbolic learner that outputs transparent, human-readable logic. Tested on the PHOTO trial dataset (n=330), TM achieved an F1-score of 0.80, outperforming XGBoost (0.78), Logistic Regression (0.60), and EORTC (0.42). TM reveals the exact clauses behind each prediction, grounded in clinical features like tumour count, surgeon experience, and hospital stay - offering accuracy and full transparency. This makes TM a powerful, trustworthy decision-support tool ready for real-world adoption.