## 3 Materials and Methods
### Machine Learning Algorithms  
In this study, we classified the stroke group and the healthy group using seven of the most widely used machine learning models based on EMG feature data collected during the 10 m walk. The machine learning models were trained and evaluated using the scikit-learn library in Python, and the results were visualized using matplotlib. In the scikit-learn library, a logistic regression was implemented using the logistic regression module, a decision tree with the decision tree classifier module, random forest with the random forest classifier module, multilayer perceptron with the MLP classifier module, support vector machine with the SVC module, naive Bayes with the Gaussian-NB module, and K-nearest neighbors with the K-neighbors classifier module. The seven machine learning models utilized in this study were selected based on their proven efficacy in learning high-dimensional and non-linear data through each mathematical operation and statistical method. As shown in Table 1, the selection of each model was supported by previous  
Figure 2: Schematic of EMG data processing and feature extraction procedures. studies that validated disease classification models using EMG data, thereby demonstrating their suitability for the dataset employed in this study. The objective was to identify the model that best learns from the EMG-driven features among these models. The definition and the equation are presented as follows:  
* Logistic regression (LR): A widely used model for binary classification problems, logistic regression represents the relationship between feature variables and the outcome using a linear function [58]. \[\text{logit}(P(Y=1))=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\cdots+\beta_{n}X_ {n}\] (1) where, \(\text{logit}(\text{P(Y=1)})\) represents the log-odds of the probability of belonging to class 1, and \(\beta_{n}\) denotes the weight for each feature variable \(X_{n}\). * Decision tree (DT): A non-parametric model that performs predictions by partitioning data based on its features. It is known for its intuitive nature and ease of interpretation [59]. \[Gini=1-\sum_{k=1}^{K}{P_{k}}^{2}\] (2) where, \(P_{k}\) represents the probability of belonging to class \(k\). * Random forest (RF): A model that improves predictive performance by assembling multiple decision trees. It is effective in preventing overfitting [60]. \[\hat{\Upsilon}=\text{mode}(\{T_{i}(X)\})\] (3) where, \(T_{i}(X)\) represents the prediction result of each tree, and the final prediction is determined by majority voting. * Multilayer perceptron (MLP): An artificial neural network model composed of multiple layers of neurons, known for its ability to effectively learn non-linear relationships [61]. \[\hat{\Upsilon}=f(\sum_{i=1}^{n}{w_{i}X_{i}}+b)\] (4) where, \(f\) represents the activation function, \(w_{i}\) denotes the weights, and \(b\) is the bias. * Support vector machine (SVM): A model that finds a hyperplane to separate data in a high-dimensional space, capable of solving non-linear problems through the use of kernel tricks [62]. \[f(x)=\text{sign}(w\cdot x+b)\] (5) where, \(w\) represents the weight vector, \(b\) is the bias, and the kernel function used is the RBF. * Naive Bayes (NB): A probabilistic model based on [PERSON] theorem, which performs predictions under the assumption that features are independent [63]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Disease**} & \multicolumn{4}{c}{**Performance (\%)**} \\ \cline{3-5}  & & **ACC** & **TPR** & **TNR** \\ \hline Logistic Regression [54] & ALS & 95.1 & 96 & 94.3 \\ Decision Tree [55] & ALS & 90.9 & 94 & 88.7 \\ Random Forest [55] & ALS & 96.7 & 99.6 & 94.8 \\ MultiLayer Perceptron [56] & Parkinson & 97 & 100 & 94 \\ Support Vector Machine [54] & ALS & 94.1 & 96 & 94.3 \\ Naive Bayes [56] & Parkinson & 97 & 100 & 94 \\ K-Nearest Neighbors [57] & NMDs & 98.8 & 98.6 & 99.4 \\ \hline \hline \end{tabular}  
* ALS: amyotrophic lateral sclerosis; NMDs: neuromuscular disorders.  
\end{table}