Íris Viana Dos Santos Santana1, Andressa Cm da Silveira2, Álvaro Sobrinho1,3, Lenardo Chaves E Silva4, Leandro Dias da Silva3, Danilo F S Santos2, Edmar C Gurjão2, Angelo Perkusich2.
Abstract
BACKGROUND: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources.
OBJECTIVE: The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.
METHODS: Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances.
RESULTS: Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model.
CONCLUSIONS: The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing. ©Íris Viana dos Santos Santana, Andressa CM da Silveira, Álvaro Sobrinho, Lenardo Chaves e Silva, Leandro Dias da Silva, Danilo F S Santos, Edmar C Gurjão, Angelo Perkusich. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.04.2021.
BACKGROUND: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources.
OBJECTIVE: The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.
METHODS: Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances.
RESULTS: Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model.
CONCLUSIONS: The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing. ©Íris Viana dos Santos Santana, Andressa CM da Silveira, Álvaro Sobrinho, Lenardo Chaves e Silva, Leandro Dias da Silva, Danilo F S Santos, Edmar C Gurjão, Angelo Perkusich. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.04.2021.
Entities:
Keywords:
COVID-19; classification models; medical diagnosis; test prioritization
Year: 2021
PMID： 33750734 DOI： 10.2196/27293
Source DB: PubMed Journal: J Med Internet Res ISSN： 1438-8871 Impact factor: 5.428