TY  - JOUR
AU  - Viana dos Santos Santana, Íris
AU  - CM da Silveira, Andressa
AU  - Sobrinho, Álvaro
AU  - Chaves e Silva, Lenardo
AU  - Dias da Silva, Leandro
AU  - Santos, Danilo F S
AU  - Gurjão, Edmar C
AU  - Perkusich, Angelo
PY  - 2021
DA  - 2021/4/8
TI  - Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach
JO  - J Med Internet Res
SP  - e27293
VL  - 23
IS  - 4
KW  - COVID-19
KW  - test prioritization
KW  - classification models
KW  - medical diagnosis
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/4/e27293
UR  - https://doi.org/10.2196/27293
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33750734
DO  - 10.2196/27293
ID  - info:doi/10.2196/27293
ER  -