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

Saram Abbas, Naeem Soomro, Rishad Shafik, Rakesh Heer, Kabita Adhikari
7/26/2025
cs.LGcs.AI

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Abstract

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 Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases.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.

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Cite this paper

@article{abbas2025aibased,
  title  = {AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines},
  author = {Saram Abbas and Naeem Soomro and Rishad Shafik and Rakesh Heer and Kabita Adhikari},
  year   = {2025},
  eprint = {2507.19803},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.19803v1}
}

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