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In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : controlling the COVID-19 outbreak in Brazil is a challenge of continental proportions 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 symptomatic patients for testing to assist the early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.

METHODS : raw data from 55,676 Brazilians were pre-processed, and the Chi-squared test was used to confirm the relevance of features: Gender, Health Professional, Fever, Sore Throat, Dyspnea, Olfactory Disorders, Cough, Coryza, Taste Disorders, and Headache. Classification models were implemented relying on pre-processed datasets, 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 are among the highest-ranked features used by 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 (e.g., mean accuracy ≥ 89.12%) assist the COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a symptomatic patient for COVID-19 testing.


Viana Dos Santos Santana Íris, C M da Silveira Andressa, Sobrinho Álvaro, Chaves E Silva Lenardo, Dias da Silva Leandro, Freire de Souza Santos Danilo, Candeia Edmar, Perkusich Angelo