Author: Andre Filipe de Moraes Batista; Joao Luiz Miraglia; Thiago Henrique Rizzi Donato; Alexandre Dias Porto Chiavegatto Filho
Title: COVID-19 diagnosis prediction in emergency care patients: a machine learning approach Document date: 2020_4_7
ID: nvavj9gk_6
Snippet: We tested the predictive performance for positive diagnosis of COVID-19 of five popular machine learning algorithms (neural networks, gradient boosted trees, random forests, logistic regression and support vector machines) with 10-fold cross validation for hyperparameter tuning . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity......
Document: We tested the predictive performance for positive diagnosis of COVID-19 of five popular machine learning algorithms (neural networks, gradient boosted trees, random forests, logistic regression and support vector machines) with 10-fold cross validation for hyperparameter tuning . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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