Open AccessClassification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach
Author(s) -
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,
Ângelo Perkusich
Publication year - 2021
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/27293
Subject(s) - artificial intelligence , machine learning , support vector machine , interpretability , random forest , computer science , decision tree , multilayer perceptron , boosting (machine learning) , gradient boosting , logistic regression , linear discriminant analysis , artificial neural network
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.