# Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification

arXiv: https://arxiv.org/abs/2601.07969 · HTML: https://mlsanity.com/paper/2601.07969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https://mlsanity.com/paper/2506.16791/) (2025)
- [Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment](https://mlsanity.com/paper/2103.13300/) (2021)
- [A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets](https://mlsanity.com/paper/2408.14817/) (2024)
- [An Overview on Audio, Signal, Speech, & Language Processing for COVID-19](https://mlsanity.com/paper/2005.08579/) (2020)
- [Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata](https://mlsanity.com/paper/2307.04842/) (2023)
- [TB or not TB? Acoustic cough analysis for tuberculosis classification](https://mlsanity.com/paper/2209.00934/) (2022)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)

## Built on by (most cited first, 1 of 1)

- [Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening](https://mlsanity.com/paper/2608.25846/) (2026)

## Abstract (arXiv)

In this paper, we propose a standardized framework for automatic tuberculosis (TB) detection from cough audio and routinely collected clinical data using machine learning. While TB screening from audio has attracted growing interest, progress is difficult to measure because existing studies vary substantially in datasets, cohort definitions, feature representations, model families, validation protocols, and reported metrics. Consequently, reported gains are often not directly comparable, and it remains unclear whether improvements stem from modeling advances or from differences in data and evaluation. We address this gap by establishing a strong, well-documented baseline for TB prediction using cough recordings and accompanying clinical metadata from a recently compiled dataset from several countries. Our pipeline is reproducible end-to-end, covering feature extraction, multimodal fusion, cougher-independent evaluation, and uncertainty quantification, and it reports a consistent suite of clinically relevant metrics to enable fair comparison. We further quantify performance for cough audio-only and fused (audio + clinical metadata) models, and release the full experimental protocol to facilitate benchmarking. This baseline is intended to serve as a common reference point and to reduce methodological variance that currently holds back progress in the field.