Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening

The study investigates why machine‑learning models for tuberculosis screening based on cough acoustics fail to generalize across datasets. Classical ML and deep‑learning classifiers achieved moderate performance within their own datasets (ROC‑AUC up to 0.755) but performed poorly on external data, often below 0.6. The authors found that audio features were more influenced by recording device and dataset than by TB status, and that device‑diverse training improved transfer while device mismatch degraded it. A clinical‑variable baseline showed more consistent generalization, suggesting acquisition‑specific variability is a stronger driver of poor generalizability than population shift.

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