Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador.
Infect Genet Evol
; 98: 105228, 2022 03.
Article
in English
| MEDLINE | ID: covidwho-1654924
ABSTRACT
The investigation of conventional complete blood-count (CBC) data for classifying the SARS-CoV-2 infection status became a topic of interest, particularly as a complementary laboratory tool in developing and third-world countries that financially struggled to test their population. Although hematological parameters in COVID-19-affected individuals from Asian and USA populations are available, there are no descriptions of comparative analyses of CBC findings between COVID-19 positive and negative cases from Latin American countries. In this sense, machine learning techniques have been employed to examine CBC data and aid in screening patients suspected of SARS-CoV-2 infection. In this work, we used machine learning to compare CBC data between two highly genetically distinguished Latin American countries Brazil and Ecuador. We notice a clear distribution pattern of positive and negative cases between the two countries. Interestingly, almost all red blood cell count parameters were divergent. For males, neutrophils and lymphocytes are distinct between Brazil and Ecuador, while eosinophils are distinguished for females. Finally, neutrophils, lymphocytes, and monocytes displayed a particular distribution for both genders. Therefore, our findings demonstrate that the same set of CBC features relevant to one population is unlikely to apply to another. This is the first study to compare CBC data from two genetically distinct Latin American countries.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Mass Screening
/
SARS-CoV-2
/
COVID-19
/
Hematologic Tests
Type of study:
Observational study
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
South America
/
Brazil
/
Ecuador
Language:
English
Journal:
Infect Genet Evol
Journal subject:
Biology
/
Communicable Diseases
/
Genetics
Year:
2022
Document Type:
Article
Affiliation country:
J.meegid.2022.105228
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