  Objective: The automatic discrimination between the coughing sounds produced
by patients with tuberculosis (TB) and those produced by patients with other
lung ailments.
  Approach: We present experiments based on a dataset of 1358 forced cough
recordings obtained in a developing-world clinic from 16 patients with
confirmed active pulmonary TB and 35 patients suffering from respiratory
conditions suggestive of TB but confirmed to be TB negative. Using nested
cross-validation, we have trained and evaluated five machine learning
classifiers: logistic regression (LR), support vector machines (SVM), k-nearest
neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks
(CNN).
  Main Results: Although classification is possible in all cases, the best
performance is achieved using LR. In combination with feature selection by
sequential forward selection (SFS), our best LR system achieves an area under
the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78
high-resolution mel-frequency cepstral coefficients (MFCCs). This system
achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the
90\% sensitivity at 70\% specificity specification considered by the World
Health Organisation (WHO) as a minimal requirement for a community-based TB
triage test.
  Significance: The automatic classification of cough audio sounds, when
applied to symptomatic patients requiring investigation for TB, can meet the
WHO triage specifications for the identification of patients who should undergo
expensive molecular downstream testing. This makes it a promising and viable
means of low cost, easily deployable frontline screening for TB, which can
benefit especially developing countries with a heavy TB burden.
