Confident COVID-19 cough prediction on imbalanced data

Julia A. Meister, Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to conferenceOtherpeer-review

Abstract

COVID cough data is heavily imbalanced, and it is challenging to collect more samples. Therefore, models are biased and their predictions cannot be trusted. In this poster, we propose a confidence measure for COVID-19 cough classification.
Original languageEnglish
Number of pages1
Publication statusPublished - 21 Nov 2022
EventMachine Learning for Healthcare - Institute of Physics, London, United Kingdom
Duration: 21 Nov 202221 Nov 2022
https://iop.eventsair.com/mlh2022/

Conference

ConferenceMachine Learning for Healthcare
Abbreviated titleMLH
Country/TerritoryUnited Kingdom
CityLondon
Period21/11/2221/11/22
Internet address

Bibliographical note

Winner of the Best Poster Award.

Keywords

  • machine learning
  • COVID-19 classification
  • imbalanced data

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