# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 6 Discussion  
In Section 4, we focused on examining the elasticity of RBCs based on data on the \(SA:V\) ratio during their flow through a channel. These data represent one of the fundamental characteristics of RBCs; they are frequently studied and can also be obtained from real experiments, not just from simulation. The data from the movement of each RBC were first reduced to four basic statistics: the maximum, minimum, average, and dispersion of the \(SA:V\) ratio. The classical KS test revealed that the maximum and average values could differentiate healthy RBCs from a combined group of damaged ones based on their elasticity. However, it is not possible to differentiate between the three groups of damaged RBCs. The minimum and dispersion values do not distinguish between RBC groups at all using the chosen statistical methods. These findings are noted as statistical facts, even though it would be possible to look for different physical justifications for the specific design of the experiment. In any case, we consider it worth noting that the possibility of distinguishing RBCs according to their elasticity found here qualitatively corresponds to the result obtained using the CNN mentioned in Section 3. Here, the classification of RBCs into four categories of elasticity was used when training the CNN, but the correct classification was achieved only for the group of healthy and aggregated group of damaged cells. Due to the fundamental difference in the tools used to achieve these results, a direct numerical comparison is not possible, but it inspires a further investigation of this phenomenon. Our experiments with decision trees in Section 5 reveal insights into the efficiency of various predictor sets and the impact of recorded sequence length on the classification accuracy. Our results suggest that increasing the length of the sequence (\(S\)) generally improves classification accuracy up to a certain point, after which the improvements starts to be negligible. This indicates that, while longer observation periods can be beneficial, excessively long monitoring may not yield proportional gains in accuracy. Specifically, monitoring cells for approximately one complete passage through the channel appears sufficient for optimal performance. The comparison between deep neural networks and decision tree-based models reveals that the latter can achieve comparable or better accuracy with simpler predictor sets. This is particularly evident in the classification into four classes, where decision trees outperformed neural networks using the second predictor set. This confirms that decision trees are a proper tool to effectively handle tabular data, providing a more interpretable and efficient alternative to neural networks. In addition to stating the numerical differences in classification accuracy, it is appropriate to note that the input data for both methods are different: the input for CNN was the video recording of the flow, while the input for decision trees was the predictors extracted from the simulation output. Video recording is based on 2D data, while predictor sets 3-6 benefit from 3D data. On the other hand, predictor sets 1 and 2 work with very simple 2D-data-based features, and one can expect that a CNN will be able to extract more information from the video. Table 7 shows the classification accuracy on the test dataset. In the case of decision tree methods, we list here only the models for \(S=80\), which approximately corresponds to one pass of a cell through the channel--that was also the case in video recordings used in CNN models. Future research can focus on several directions to further improve the classification of RBC elasticity. One potential way is the integration of various data sources, such as combining video recordings with additional sensors capturing the mechanical properties or biochemical markers of RBCs. This could provide a richer dataset and better classification accuracy.