# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 6 Discussion
Performing experimental validation using actual RBC samples will be essential to confirm the theoretical findings and translate them into practical diagnostic tools. **Author Contributions:** Conceptualization, K.B., H.B. and P.N. ; methodology, H.B., P.N. and M.S. ; software, S.M., P.N. and M.S. ; validation, S.M. and P.N. ; formal analysis, H.B. and P.N. ; investigation, H.B., S.M. and P.N. ; resources, M.S. and H.B. ; data curation, S.M., P.N. and M.S. ; writing--original draft preparation, H.B., S.M., P.N. and M.S. ; writing--review and editing, H.B., K.B., P.N., S.M. and M.S. ; visualization, S.M., P.N. and M.S. ; supervision, K.B. ; project administration, K.B. ; funding acquisition, K.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Ministry of Education, Science, Research, and Sport of the Slovak Republic under contract No. VEGA 1/0369/22. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. \begin{table}
\begin{tabular}{c c c c c} \hline
**Training Number of Classes** & **Testing Number of Classes** & **Model** & **Predictors** & **Accuracy** \\ \hline \hline \multirow{8}{*}{4} & \multirow{8}{*}{4} & CNN & video recordings & 55.48\% \\  & & XGB & set 1 & 45.98\% \\  & & RF & set 1 & 45.27\% \\  & & XGB & set 2 & 60.81\% \\  & & RF & set 2 & 60.39\% \\  & & XGB & set 3 & 63.64\% \\  & & RF & set 3 & 62.85\% \\  & & XGB & set 4 & 69.16\% \\  & & RF & set 4 & 67.51\% \\  & & XGB & set 5 & 91.14\% \\  & & RF & set 5 & 86.73\% \\  & & XGB & set 6 & 93.90\% \\  & & RF & set 6 & 89.96\% \\ \hline \multirow{8}{*}{2} & \multirow{8}{*}{2} & CNN & video recordings & 61.72\% \\  & & XGB & set 1 & 82.81\% \\  & & RF & set 1 & 82.35\% \\  & & XGB & set 2 & 92.00\% \\  & & RF & set 2 & 91.39\% \\  & & XGB & set 3 & 93.32\% \\  & & RF & set 3 & 93.27\% \\  & & XGB & set 4 & 96.50\% \\  & & RF & set 4 & 95.55\% \\  & & XGB & set 5 & 98.04\% \\  & & RF & set 5 & 98.32\% \\  & & XGB & set 6 & 98.97\% \\  & & RF & set 6 & 99.08\% \\ \hline \hline \multirow{8}{*}{4} & \multirow{8}{*}{2} & CNN & video recordings & 93.91\% \\  & & XGB & set 1 & 81.57\% \\  & & RF & set 1 & 80.89\% \\  & & XGB & set 2 & 91.55\% \\  & & RF & set 2 & 90.60\% \\  & & XGB & set 3 & 93.10\% \\  & & RF & set 3 & 93.00\% \\  & & XGB & set 4 & 96.03\% \\  & & RF & set 4 & 95.23\% \\  & & XGB & set 5 & 97.92\% \\  & & RF & set 5 & 97.93\% \\  & & XGB & set 6 & 98.92\% \\  & & RF & set 6 & 98.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the classification accuracy of convolutional neural network (CNN) and decision tree-based models (with \(S=80\)). **Data Availability Statement:** The original data presented in the study are openly available on GitHub at [[https://github.com/nofto/RBC-Elasticity-Classification](https://github.com/nofto/RBC-Elasticity-Classification)]([https://github.com/nofto/RBC-Elasticity-Classification](https://github.com/nofto/RBC-Elasticity-Classification)) (accessed on 31 July 2024). However, the complete output from the simulation is too large for public sharing capabilities and is available on request from the corresponding author. The open-source version of the ESPResSo software package with the implemented PyOIF module used in this study is available in GitHub at [[https://github.com/icimrak/espresso/tree/python](https://github.com/icimrak/espresso/tree/python)]([https://github.com/icimrak/espresso/tree/python](https://github.com/icimrak/espresso/tree/python)) (accessed on 31 July 2024). **Acknowledgments:** We would like to acknowledge the use of ChatGPT, a generative AI tool, in the preparation of this manuscript. As non-native English speakers, we utilized ChatGPT to assist in the better formulation of several phrases. It is important to note that AI was not employed in the creation of any substantive content within this manuscript.