Automatic cough classification for tuberculosis screening in a real-world environment
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, k-nearest neighbour, multilayer perceptrons and convolutional neural networks. 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, 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-r...
Source: Physiological Measurement - Category: Physiology Authors: Madhurananda Pahar, Marisa Klopper, Byron Reeve, Rob Warren, Grant Theron and Thomas Niesler Source Type: research
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