ArticleBioengineering (Basel, Switzerland)2024
Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Energy Measures as Biomarkers of SARS-CoV-2 Variants and Receptors.Bioengineering (Basel, Switzerland) · 2026Article
- Arboviruses and COVID-19: Global Health Challenges and Human Enhancement Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.Bioinformatics (Oxford, England) · 2025Article
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7 authors.
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Abstract
backgroundSevere 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.
methodsThis 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.
resultsThree 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%.
conclusionsThe 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.
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