  We present first results showing that it is possible to automatically
discriminate between the coughing sounds produced by patients with tuberculosis
(TB) and those produced by patients with other lung ailments in a real-world
noisy environment. Our experiments are based on a dataset of cough recordings
obtained in a real-world clinic setting from 16 patients confirmed to be
suffering from TB and 33 patients that are suffering from respiratory
conditions, confirmed as other than TB. We have trained and evaluated several
machine learning classifiers, including logistic regression (LR), support
vector machines (SVM), k-nearest neighbour (KNN), multilayer perceptrons (MLP)
and convolutional neural networks (CNN) inside a nested k-fold cross-validation
and find that, although classification is possible in all cases, the best
performance is achieved using the LR classifier. In combination with feature
selection by sequential forward search (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 WHO as minimal
requirements for community-based TB triage test. We conclude that automatic
classification of cough audio sounds is promising as a viable means of low-cost
easily-deployable front-line screening for TB, which will greatly benefit
developing countries with a heavy TB burden.
