AI in Medicine

Disentangling Overfitting From Biological Signal: A Responsible AI Framework for IBS Analysis

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Abstract

Machine learning applied to Irritable Bowel Syndrome (IBS) frequently reports high diagnostic performance despite small cohorts and heterogeneous preprocessing. We present a four-stage responsible AI workflow applied to the MARS-IBS-2020 dataset (n = 73 at baseline), demonstrating that leakage ablation, harmonized preprocessing, and learning-curve diagnostics expose structural overfitting behind nominally high ROC-AUC values. A single evaluation yielded AUC 0.985 under temporal filtering, yet bootstrapped resampling (N = 50) reveals a mean AUC of only 0.63 with 95% CI spanning [0.10, 1.00], and memorisation persists across all sample sizes. SHAP attribution consistently surfaces bile acid and tryptophan metabolites as dominant predictors, aligning with established IBS biology. The study offers a transparent methodological template for evaluating small-cohort biomedical ML systems where traditional performance metrics are unreliable.

How to Cite:

Satriani, N., (2026) “Disentangling Overfitting From Biological Signal: A Responsible AI Framework for IBS Analysis”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 12-16. doi: https://doi.org/10.34749/3061-1466.2026.2