Ensemble learning model for diagnosing COVID-19 from routine blood tests

Maryam AlJame; Imtiaz Ahmad; Ayyub Imtiaz; Ameer Mohammed

doi:10.1016/j.imu.2020.100449

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Ensemble learning model for diagnosing COVID-19 from routine blood tests

Authors

AlJame, Maryam¹
Ahmad, Imtiaz¹
Imtiaz, Ayyub²
Mohammed, Ameer¹

¹ Computer Engineering Department, Kuwait University, Kuwait
² College of Medicine, Kuwait University, Kuwait

Type

Published Article

Journal

Informatics in Medicine Unlocked

Publisher

The Author(s). Published by Elsevier Ltd.

Publication Date

Oct 20, 2020

Volume

21

Pages

100449–100449

Identifiers

DOI: 10.1016/j.imu.2020.100449

PMID: 33102686

PMCID: PMC7572278

Source

PubMed Central

Keywords

License

Unknown

Abstract

• State-of-the-art review on emerging machine learning techniques for diagnosing COVID-19 from routine laboratory and clinical data. • Proposed an ensemble learning model for initial screening of COVID-19 patients from routine blood tests. • Achieved high accuracy of 99.88% in distinguishing COVID-19 positive cases from normal ones. • ERLX model provides more accessible and less costly alternative tool for an accurate early screening of COVID-19 patients.

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