Confident COVID-19 cough prediction on imbalanced data

Julia A. Meister, Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to conferenceOtherpeer-review


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


ConferenceMachine Learning for Healthcare
Abbreviated titleMLH
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

Winner of the Best Poster Award.


  • machine learning
  • COVID-19 classification
  • imbalanced data


Dive into the research topics of 'Confident COVID-19 cough prediction on imbalanced data'. Together they form a unique fingerprint.

Cite this