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

Íris Viana Dos Santos Santana¹, Andressa Cm da Silveira², Álvaro Sobrinho^1,3

¹Federal University of the Agreste of Pernambuco, Garanhuns, Brazil.

Journal of Medical Internet Research

|March 22, 2021

Summary

A decision tree model effectively prioritizes COVID-19 testing in Brazil, achieving over 89% accuracy. This approach aids early detection by identifying symptomatic patients for testing, addressing challenges in Brazil.

Keywords:

COVID-19 classification models medical diagnosis test prioritization

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Area of Science:

Computational epidemiology
Machine learning in public health
Infectious disease modeling

Background:

Controlling COVID-19 in Brazil is difficult due to population density and testing inefficiencies.
Limited testing resources and social distancing challenges hinder effective outbreak management.

Purpose of the Study:

To develop a model for prioritizing symptomatic patients for COVID-19 testing in Brazil.
To improve early detection and control strategies through efficient testing resource allocation.

Main Methods:

Utilized chi-square tests to identify relevant features (gender, fever, respiratory symptoms).
Implemented and compared supervised learning classification algorithms including Decision Tree (DT), Random Forest, and Gradient Boosting.
Evaluated model performance using 10-fold cross-validation and statistical tests.

Main Results:

Gender, fever, and dyspnea were key predictors in high-performing models (MLP, GBM, DT, RF, XGBoost, SVM).
Decision Tree (DT) emerged as the most suitable model due to its interpretability and high performance.
The DT model achieved a mean accuracy of ≥89.12%.

Conclusions:

The Decision Tree (DT) classification model offers an effective solution for prioritizing COVID-19 testing in Brazil.
This model can guide the prioritization of symptomatic individuals for timely diagnostic testing.
The findings support enhanced public health strategies for infectious disease control.