Confident COVID-19 cough prediction on imbalanced data

Julia A. Meister; Khuong An Nguyen; Zhiyuan Luo

Confident COVID-19 cough prediction on imbalanced data

Julia A. Meister
, Khuong An Nguyen
, Zhiyuan Luo

Research output: Contribution to conference › Paper › peer-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 language	English
Publication status	Published - 21 Nov 2022

Keywords

machine learning
COVID-19 classification
imbalanced data

Access to Document

https://iop.eventsair.com/mlh2022/

Cite this

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title = "Confident COVID-19 cough prediction on imbalanced data",

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.",

keywords = "machine learning, COVID-19 classification, imbalanced data",

author = "Meister, \{Julia A.\} and Nguyen, \{Khuong An\} and Zhiyuan Luo",

note = "Winner of the Best Poster Award.; Machine Learning for Healthcare, MLH ; Conference date: 21-11-2022 Through 21-11-2022",

year = "2022",

month = nov,

day = "21",

language = "English",

}

TY - CONF

T1 - Confident COVID-19 cough prediction on imbalanced data

AU - Meister, Julia A.

AU - Nguyen, Khuong An

AU - Luo, Zhiyuan

N1 - Winner of the Best Poster Award.; Machine Learning for Healthcare, MLH ; Conference date: 21-11-2022 Through 21-11-2022

PY - 2022/11/21

Y1 - 2022/11/21

N2 - 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.

AB - 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.

KW - machine learning

KW - COVID-19 classification

KW - imbalanced data

M3 - Paper

ER -