Version 1
: Received: 20 August 2024 / Approved: 21 August 2024 / Online: 22 August 2024 (09:16:39 CEST)
How to cite:
Bachratý, H.; Novotný, P.; Smiešková, M.; Bachratá, K.; Molčan, S. Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity. Preprints2024, 2024081610. https://doi.org/10.20944/preprints202408.1610.v1
Bachratý, H.; Novotný, P.; Smiešková, M.; Bachratá, K.; Molčan, S. Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity. Preprints 2024, 2024081610. https://doi.org/10.20944/preprints202408.1610.v1
Bachratý, H.; Novotný, P.; Smiešková, M.; Bachratá, K.; Molčan, S. Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity. Preprints2024, 2024081610. https://doi.org/10.20944/preprints202408.1610.v1
APA Style
Bachratý, H., Novotný, P., Smiešková, M., Bachratá, K., & Molčan, S. (2024). Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity. Preprints. https://doi.org/10.20944/preprints202408.1610.v1
Chicago/Turabian Style
Bachratý, H., Katarína Bachratá and Samuel Molčan. 2024 "Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity" Preprints. https://doi.org/10.20944/preprints202408.1610.v1
Abstract
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.
Keywords
red blood cell; elasticity; machine learning; surface-area-to-volume ratio; decision trees
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
