Machine learning applied in SARS-CoV-2 COVID 19 screening using clinical analysis parameters

Authors

Keywords:

artificial intelligence, machine learning, artificial neural networks, random forest, multilayer perceptron, support vector machine, COVID-19 diagnostic aid model

Abstract

COVID-19 was considered a pandemic by the World Health Organization. Since then, world governments have coordinated information flows and issued guidelines to contain the overwhelming effects of this disease. At the same time, the scientific community is continually seeking information about transmission mechanisms, the clinical spectrum of the disease, new diagnoses, and strategies for prevention and treatment. One of the challenges is performing the tests for the diagnosis of the disease, whose technique adopted for the detection of the genetic material of COVID-19 requires equipment and specialized human resources, making it an expensive procedure. We hypothesize that machine learning techniques can be used to classify the test results for COVID-19 through the joint analysis of popular laboratory tests' clinical parameters. Machine learning techniques, such as Random Forest, Multi-Layer Perceptron, and Support Vector Machines Regression, enable the creation of disease prediction models and artificial intelligence techniques to analyze clinical parameters. Thus, we evaluated the existing correlations between laboratory parameters and the result of the COVID-19 test, and developed two classification models: the first classifies the test results for patients with suspected COVID-19, and the second classifies the hospitalization units of patients with COVID-19, both according to the laboratory parameters. The models achieved an accuracy above 96%, showing that they are promising to the classification of tests for COVID-19 and screen patients by hospitalization unit.

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Author Biographies

Rodrigo Felipe Albuquerque Paiva de Oliveira, Universidade de Pernambuco

Rodrigo Felipe A. P. de Oliveira was born in Recife, Brazil, in 1983. He has worked in the Information Technology area since 2001 with an emphasis on Artificial Intelligence, Cloud Computing and Information Security. He is a PhD student and Master in Computer Engineering from UPE, having graduated in Computer Science from UFRPE. He started his entrepreneurial career in 2011 having founded three Startups, two of which have been in operation until the present moment: Teslabit, startup of intelligent energy management for companies and Pickcells, startup of disease detection with Artificial Intelligence. He currently holds the exclusive role of Chief Technology Officer at Pickcells.

Carmelo Jose Albanez Bastos Filho, Universidade de Pernambuco

Prof. Carmelo J. A. Bastos-Filho was born in Recife, Brazil, in 1978. He received the B.Sc. in electronics engineering and the M.Sc. and Ph.D. degrees in electrical engineering from Federal University of Pernambuco (UFPE) in 2000, 2003, and 2005, respectively. In 2006, he received the best Brazilian thesis award in electrical engineering. His interests are related to: the development of protocols and algorithms to manage and to design optical communication networks, development of novel swarm intelligence algorithms and the deployment of swarm intelligence for complex optimization and clustering problems, development and application of multiobjective optimization and many-objective optimization for real-world problems, Developement and application of soft deep learning techniques, development of solutions using network sciences for big data, applications in robotics and applications in biomedical problems. He is currently an Associate Professor at the Polytechnic School of the University of Pernambuco. He was the scientist-in-chief of the technological Park for Electronics and Industry 4.0 of Pernambuco from 2016-2020. He is the director for innovation parks and graduate studies of Pernambuco. He is also the coordinator of the national innitiative for promoting ICT in Pernambuco jointly with the ministry for regional development. He is an IEEE senior member and a Research fellow level 1D of the National Research Council of Brazil (CNPq). He published roughly 300 full papers in journals and conferences and advised over 50 PhD and MSc candidates. More information at http://scholar.google.com/citations?user=t3A96agAAAAJ&hl=en

Ana Clara A. M. V. F. de Medeiros, Pickcells

Ana Clara A. M V. F de Medeiros was born in Recife, Brazil, in 1996. She graduated in Biomedicine at the Federal University of Pernambuco, in 2017. Actualy she completed postgraduate studies in Molecular Biology (2019) and in Clinical Analysis and Microbiology (2020). She currently holds an MBA in Digital Transformation and Innovation from BBI of Chicago. She work as biomedical at Pickcells where with a focus on use of automation and innovation for clinical analysis using technology to solve health problems, in addition to occupying the position of technical leader in the health area.

Pedro Jose Buarque Lins dos Santos, Universidade de Pernambuco

Pedro Buarque holds a Master's degree in Computer Engineering from the University of Pernambuco (UPE) and a Bachelor's degree in Computer Engineering from the University of Pernambuco (UPE). It operates with focus on the use of Artificial Intelligence applied in healthcare and also uses Swarm Intelligence in engineering applications. He participated in the sandwich graduation program in the Science Without Borders program funded by CAPES, where he studied for one year at Washington University in St. Louis. He participated in summer research during Science Without Borders at the Illinois Institute of Technology where he did research in the area of Algorithms for Support Big Data Infrastructure.

Daniela Lopes Freire, Universidade de São Paulo

Post-doctorate student in Artificial Intelligence at the Institute of Mathematical and Computer Sciences, University of São Paulo (ICMC-USP), PhD in Mathematical Modeling from the Northwest Regional University of the State of Rio Grande do Sul - UNIJUÍ, with a CAPES scholarship and in Information Sciences and Technologies from the Unisersitário de Lisboa Institute (ISCTE-IUL). He develops research in the field of Enterprise Application Integration, Optimization Metaheuristics, Runtime Systems and Artificial Intelligence. Master in Professional Master in Software Engineering by the Center for Advanced Studies and Systems of Recife, in 2013, Recife-PE (Brazil). Developed research in the area of interactive digital books, requirements engineering and domain analysis for software family. Graduated in Electronic Engineering from Universidade Federal de Pernambuco in 1997, Recife-PE (Brazil). In the corporate area, she has more than 17 years of experience in Systems Development, where she acted as programmer, systems analyst and project manager.

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Published

2021-06-08

How to Cite

Albuquerque Paiva de Oliveira, R. F., Bastos Filho, C. J. A., A. M. V. F. de Medeiros, A. C., Buarque Lins dos Santos, P. J., & Lopes Freire, D. (2021). Machine learning applied in SARS-CoV-2 COVID 19 screening using clinical analysis parameters. IEEE Latin America Transactions, 19(6), 978–985. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/4368

Issue

Vol. 19 No. 6 (2021): Special Issue on Fighting Against COVID-19

Section

Special Issue on Fighting against COVID-19

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