Deep forest model for diagnosing COVID-19 from routine blood tests.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 08 2021
17 08 2021
Historique:
received:
27
05
2021
accepted:
03
08
2021
entrez:
18
8
2021
pubmed:
19
8
2021
medline:
25
8
2021
Statut:
epublish
Résumé
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.
Identifiants
pubmed: 34404838
doi: 10.1038/s41598-021-95957-w
pii: 10.1038/s41598-021-95957-w
pmc: PMC8371014
doi:
Types de publication
Comparative Study
Evaluation Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16682Informations de copyright
© 2021. The Author(s).
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