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Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients

Authors :
Cosimo Aliani
Eva Rossi
Mateusz Soliński
Piergiorgio Francia
Antonio Lanatà
Teodor Buchner
Leonardo Bocchi
Source :
Bioengineering, Vol 11, Iss 9, p 952 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. Methods: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Results: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. Conclusions: The study’s results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.

Details

Language :
English
ISSN :
11090952 and 23065354
Volume :
11
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.0c444f5097484d2f84d2c7057d7d9d12
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering11090952