ArticleJournal of medical Internet research2021
Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2022Pooled it
- Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.BioMed research international · 2022Pooled it
- Unbiased inference for echocardiogram urgency prediction using double machine learning.PloS one · 2026Article
- Predicting nephrotic syndrome outcomes in children using machine learning: insights from a cross-sectional study.BMC nephrology · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Radiomics models to predict bone marrow metastasis of neuroblastoma using CT.Cancer innovation · 2024Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning.Interdisciplinary sciences, computational life sciences · 2022Article
- COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification.Diagnostics (Basel, Switzerland) · 2022Article
- Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study.JMIR medical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.