# Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection
## 6 Discussion  
The proposed FSC4 RBF system can be a helpful supporting tool for doctors or other specialists to diagnose the hemiplegia type between healthy and non-healthy individuals playing a significant role in their rehabilitation plan. The experimental results presented in Section 5.2 validated that the optimized features created from the FSC4 RBF algorithm significantly increased the classification accuracy of RBFNN. The RBFNN using the initial features had only a 74.53% classification accuracy, the lowest compared to FSC4 RBF and the other three classifiers. The results in Table 2 revealed that the BFGS, SVM, GenClass, and FSC4 RBF algorithms had an average of 9.18%, 6.16%, 13.45%, and 15.54% performance increase in their test set results compared to the RBFNN without feature construction. All methods were run one additional time to create their confusion matrices. In each confusion matrix, the green boxes denote the samples (bold fonts) and their percentages which are classified correctly. In contrast, the misclassified samples (bold fonts) and their percentages are visualized with red boxes. The vertical white boxes show the percentages of all the samples predicted to belong to each class classified correctly (green color) and incorrectly (red color). The horizontal white boxes show the percentages of all the examples belonging to each class and are classified correctly (green color) and incorrectly (red color). Finally, the overall accuracy is visualized with a gray box. The confusion matrix of the FC4 RBF method is depicted in Figure 5. It is shown that FC4 RBF had the highest classification accuracy (92%) in the right hemiplegia class, while the lowest (76%) was in the normal class. Figure 4: Comparison results plot. This figure visualizes the classification accuracy between the proposed FSC4 RBF algorithm and four existing methods. It is shown that the proposed method managed to achieve the highest accuracy (black bar) in the custom-created dataset. Similarly, in the four confusion matrices of the compared methods in Figure 6, the highest accuracies were achieved in the right hemiplegia class, while the lowest ones were observed in the normal class. One exception was the SVM method, where the lowest accuracy was observed in the left hemiplegia class. Creating optimized features using a hybrid approach combining an RBFNN with a GE algorithm resulted in the RBFNN's classification accuracy increase. The main issue of using this approach was the significant computational overhead introduced by the GE method, which required many networks to be trained at each generation. The solution to this problem can be adopting a parallel processing scheme using a multi-core central processing unit at each algorithm's iteration. The experimental results from the proposed FSC4 RBF system were compared with existing approaches for hemiplegic gait detection. The hemiplegic gait detection method by [PERSON] et al. [8] utilized the random forest (RF) algorithm for the classification task between hemiplegic and normal gait. The RF method managed to obtain 100% classification accuracy. The authors in [PERSON] et al. [10] tested one shallow and three deep architectures for detecting hemiplegic gait. The deep architectures were based on visual geometry group (VGG)-16 CNN. The best accuracy (99.68% on the validation set) was achieved from the third DNN. [PERSON] [11] got a 100% classification accuracy using a fuzzy logic-based method. [PERSON] et al. [15] used the same algorithm with different combinations of extracted features achieving a 90.65% average accuracy between all gait feature combinations and a 95.45% best accuracy. The study [PERSON] et al. [20] used DNN architectures to detect stroke gaits and classify abnormal gait patterns. The created models achieved a 99.35% classification accuracy in detecting stroke gaits. The above methods managed to obtain impressive results, with some even reaching a 100% classification accuracy compared to the 90.07% of the proposed FSC4 RBF method.