Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data an...

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Bibliographic Details
Published in:Applied sciences 2024-10, Vol.14 (20), p.9315
Main Authors: Bachratý, Hynek, Novotný, Peter, Smiešková, Monika, Bachratá, Katarína, Molčan, Samuel
Format: Article
Language:English
Subjects:
Automation
Blood
Comparative analysis
decision trees
elasticity
Machine learning
Malaria
Mechanical properties
Mechanization
Methods
Microfluidics
Neural networks
Physiology
red blood cell
Simulation
Simulation methods
surface-area-to-volume ratio
Viscosity
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Summary:The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov–Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14209315