Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19

• URL: http://arxiv.org/abs/2212.08571v1
• Date: Thu, 15 Dec 2022 13:50:13 GMT
• Title: Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
• Authors: Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Bj\"orn W. Schuller, Ana Tendero-Ca\~nadas, Tracey Thornley, Alexander Titcomb
• Abstract summary: This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features.
• Score: 45.216628450147034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

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