Covid-19 rapid test by combining a Random Forest-based web system and blood tests
| Autor: | Rodrigo Gomes de Souza, Ricardo Emmanuel de Souza, Raquel Bezerra Calado, Luiz Alberto Reis Mattos Junior, Jeniffer Emidio de Almeida Albuquerque, Claudio Roberto Bertoldo Junior, Juliana Carneiro Gomes, Valter Augusto de Freitas Barbosa, Wellington Pinheiro dos Santos, Ricardo Juarez Escorel de Araujo, Maíra Araújo de Santana, Clarisse Lins de Lima |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) Covid-19 rapid test Disease medicine.disease_cause COVID-19 Testing Artificial Intelligence Structural Biology medicine Humans Molecular Biology Coronavirus Random Forest Hematologic Tests SARS-CoV-2 machine learning for diagnosis Public health COVID-19 Outbreak General Medicine medicine.disease software-based rapid test Random forest Test (assessment) Geography Web system computer-aided diagnosis Medical emergency blood tests Research Article |
| Zdroj: | Journal of Biomolecular Structure & Dynamics article-version (VoR) Version of Record |
| ISSN: | 1538-0254 0739-1102 |
| Popis: | The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. Communicated by Ramaswamy H. Sarma |
| Databáze: | OpenAIRE |
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