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Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Medicine and Health

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Y. Zoabi, S. Deri-rozov, et al.

Discover a groundbreaking machine-learning model that predicts COVID-19 infection with remarkable accuracy using easily accessible features. This innovative research, conducted by Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron from Tel Aviv University, aims to enhance COVID-19 testing efficiency, especially in resource-limited settings.

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Playback language: English
Abstract
This paper presents a machine-learning model that predicts COVID-19 infection using eight easily accessible features: sex, age ≥60 years, known contact with an infected individual, and five initial clinical symptoms (cough, fever, sore throat, shortness of breath, headache). The model, trained on Israeli Ministry of Health data from 51,831 individuals, achieved high accuracy (0.90 auROC) on a subsequent week's test set (47,401 individuals). The model can prioritize COVID-19 testing when resources are limited.
Publisher
npj Digital Medicine
Published On
Jan 04, 2021
Authors
Yazeed Zoabi, Shira Deri-Rozov, Noam Shomron
Tags
COVID-19
machine learning
prediction model
clinical symptoms
health data

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