Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach.

Íris Viana Dos Santos Santana,Andressa Cm Da Silveira,Álvaro Sobrinho,Danilo F S Santos,Leandro Dias Da Silva,Edmar C Gurjão,Angelo Perkusich,Lenardo Chaves E Silva

doi:10.2196/27293

Abstract

BackgroundControlling 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.ObjectiveThe 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.MethodsRaw 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.ResultsGender, 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.ConclusionsThe 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.

Highlights

Overview In modern medical systems, health care professionals, managers, and governments use information and data analysis to make decisions [1]
Such insights are relevant for health care professionals and policy makers who envision applying a classification model to prioritize patients who are symptomatic for testing
The implementations of classification models using the multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR) algorithms are available in the GitHub repository [12]

Summary

Introduction

Overview In modern medical systems, health care professionals, managers, and governments use information and data analysis to make decisions [1]. There are propositions of eHealth and mobile health (mHealth) systems to assist health care professionals and policy makers with decision making [2,3]. Such systems are relevant to provide decision support advice based on patients’ data, https://www.jmir.org/2021/4/e27293 XSLFO RenderX. People who live in low- and middle–income settings, remote settings, and hard-to-reach settings are the most affected by precarious health care. Such a situation is even more critical in a pandemic scenario. 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

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Discussion

Conclusion