# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 5 Classification Using Decision Trees
### Classification Results  
Let us first have a look at the classification accuracy based on the value of \(S\), that is, let us analyze the effect of the number of steps that we monitor on each cell. From the plots in Figures 7 and 8, it can be concluded that increasing \(S\) has a positive effect up to the level around the values of \(S=160\) or 320, from which the classification accuracy ceases to improve continuously. Thus, it seems pointless to track the cell for significantly longer than one pass through the channel (approx. 100,000 simulation steps). Figure 7: Dependence of classification results on \(S\) when predicting 4 classes. The plots in Figures 9 and 10 show how the classification accuracy changes, depending on which set of predictors we use. Especially when classifying into four classes, a significant improvement in accuracy can be observed when moving from the fourth set to the fifth set. On the other side, the effect of the third dimension (the transition from the second set to the third set) does not seem to be very significant. In all Figures 7-10, the dashed line shows the accuracy that we achieved in the classification using deep neural networks in [17]. Within that approach, we observed each cell during one passage through the channel, which corresponds approximately to a model with \(S=80\). In Figure 9 in the middle plot, it can be seen that, when classifying into four classes, we outperformed the neural networks' results with the second predictor set. Similarly, the middle plot in Figure 10 shows that, in the case of two classes, we outperformed neural networks with the fourth predictor set. In [17], we observed a fairly significant difference in the result between a pair of models created for binary classification (healthy/diseased cell). The model that was trained directly for the classification of two classes achieved less accuracy (slightly above 60%) than the model trained for the classification of four classes, in which we determined the resulting binary classification only by merging the classes containing diseased cells with three different stiffness levels into one class (accuracy above 90%). This strange phenomenon did not appear when decision trees were used--in Figures 8 and 10, it can be seen that the  
Figure 8: Dependence of classification results on \(S\) when predicting 2 classes. accuracy of models trained for the classification of four classes (blue and red color) does not differ significantly from the accuracy of models trained directly for binary classification (yellow and purple color). A comparison of random forest (RF) and gradient decision trees (XGB) does not come out significantly in favor of either method--as can be seen from Figures 7-10, the results are very similar to each other. One can notice that, in the case of classification into four classes (Figure 7), XGB outperforms RF on predictor sets 4-6 for \(S\leq 160\), but its classification accuracy drops for \(S\geq 320\). We believe the reason is that XGB is more prone to overfitting than RF and, for higher values  
To see which predictors have the biggest impact on the classification, let us take a closer look at the models for \(S=80\) (i.e., those where we observe approximately one passage of a cell through the channel) with the sixth set of predictors and the fourth set of predictors. For these models in Figures 11 and 12, we present the significance of the predictors calculated by the permutation_importance method from the sklearn library. This is a technique used to evaluate the significance of each predictor in a trained machine learning model. This method rates how much each predictor contributes to the model's classification accuracy by measuring the change in the accuracy when the values of the single predictor are randomly shuffled along the entire data. It was first described in [32].