Confident COVID-19 cough prediction on imbalanced data

Julia A. Meister, Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to conferencePaperpeer-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
Publication statusPublished - 21 Nov 2022


  • machine learning
  • COVID-19 classification
  • imbalanced data

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