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 preprocess-
ing. 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.