Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection-Reference-Cited by-同舟云学术

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Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

Published:2024-01 Issue:1 Volume:10 Page:e23219
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Rufino Jesús,Ramírez Juan Marcos^ORCID,Aguilar Jose,Baquero Carlos,Champati Jaya^ORCID,Frey Davide^ORCID,Lillo Rosa Elvira^ORCID,Fernández-Anta Antonio^ORCID

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference57 articles.

1. Global monitoring of the impact of the Covid-19 pandemic through online surveys sampled from the facebook user base;Astley;Proc. Natl. Acad. Sci.,2021

2. Coronavirus disease 2019 symptoms and severe acute respiratory syndrome coronavirus 2 antibody positivity in a large survey of first responders and healthcare personnel, May-July 2020;Akinbami;Clin. Infect. Dis.,2021

3. M. Klompas, Coronavirus disease 2019 (Covid-19): protecting hospitals from the invisible, 2020.

4. Machine learning-based prediction of Covid-19 diagnosis based on symptoms;Zoabi;npj Digit. Med.,2021

5. A symptom-based rule for diagnosis of Covid-19;Smith;SN Compr. Clin. Med.,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. COVID-19 seroprevalence estimation and forecasting in the USA from ensemble machine learning models using a stacking strategy;Expert Systems with Applications;2024-12

2. Machine learning predictive modeling of the persistence of post-Covid19 disorders: Loss of smell and taste as case studies;Heliyon;2024-08

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