Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
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.
Cite
@article{arxiv.2212.08571,
title = {Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19},
author = {Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G. Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and Jonathon Mellor and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J. Roberts and Björn W. Schuller and Ana Tendero-Cañadas and Tracey Thornley and Alexander Titcomb},
journal= {arXiv preprint arXiv:2212.08571},
year = {2023}
}