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

• URL: http://arxiv.org/abs/2608.25846v1
• Date: Wed, 26 Aug 2026 14:22:32 GMT
• Title: Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
• Abstract summary: Cough acoustics are promising for non-invasive tuberculosis (TB) screening.<n>Machine learning (ML) models capture disease-related acoustics or artifacts of data collection.<n>We evaluated the cross-dataset generalizability of classical ML and deep learning (DL) cough-based TB classifiers across three independent datasets.
• Score: 3.7861325199095757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collection remains unresolved. We evaluated the cross-dataset generalizability of classical ML and deep learning (DL) cough-based TB classifiers across three independent datasets. Despite moderate within-dataset performance (ROC-AUC up to $0.755 \pm 0.056$), both pipelines fail to generalize, with external performance frequently below 0.6, indicating a possible limitation of the data. We further observed audio representations are organized by recording device and dataset rather than TB status, predicted TB probability tracks country-level prevalence in CODA, and device mismatch degrades transfer while device-diverse training improves it. Additionally, a clinical-variable baseline generalizes more consistently (ROC-AUC $0.655 - 0.711$), indicating acquisition-specific variability is a stronger driver of poor generalizability than population shift. High within-dataset performance is not enough. External validation is essential before cough-based TB models are clinically ready.

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