## 5 Discussion  
This study examined the performance and feature importance of ML models trained on key statistical features of EMG data collected from both legs of stroke patients and \begin{table}
\begin{tabular}{c c c} \hline \hline
**True** & **Prediction** & **Healthy** & **Stroke** \\ \hline Healthy & 21 & 0 \\ Stroke & 0 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the test data. Figure 3: Variables with high feature importance in the RF model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Validation**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-9}  & **ACC** & **TPR** & **TNR** & **PPV** & **ACC** & **TPR** & **TNR** & **PPV** \\ \hline Logistic Regression & 0.96 & 0.96 & 0.96 & 0.96 & 0.92 & 0.97 & 0.90 & 0.97 \\ Decision Tree & 0.94 & 0.93 & 0.96 & 0.96 & 0.96 & 1.00 & 0.90 & 0.78 \\ Random Forest & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ MultiLayer Perceptron & 0.99 & 0.99 & 0.99 & 0.99 & 0.98 & 0.93 & 0.95 & 0.96 \\ Support Vector Machine & 0.94 & 0.97 & 0.66 & 0.72 & 0.94 & 0.93 & 0.76 & 0.83 \\ Naive Bayes & 0.77 & 0.93 & 0.63 & 0.69 & 0.77 & 0.93 & 0.57 & 0.74 \\ K-Nearest Neighbors & 0.85 & 0.97 & 0.75 & 0.77 & 0.96 & 0.96 & 0.95 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of each model on validation and test data, where stroke patients are classified as positive cases. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Validation**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-9}
**Model** & **ACC** & **TPR** & **TNR** & **PPV** & **ACC** & **TPR** & **TNR** & **PPV** \\ \hline Logistic Regression & 0.96 & 0.96 & 0.96 & 0.96 & 0.92 & 0.97 & 0.90 & 0.97 \\ Decision Tree & 0.94 & 0.93 & 0.96 & 0.96 & 0.96 & 1.00 & 0.90 & 0.78 \\ Random Forest & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ MultiLayer Perceptron & 0.99 & 0.99 & 0.99 & 0.99 & 0.98 & 0.93 & 0.95 & 0.96 \\ Support Vector Machine & 0.94 & 0.97 & 0.66 & 0.72 & 0.94 & 0.93 & 0.76 & 0.83 \\ Naive Bayes & 0.77 & 0.93 & 0.63 & 0.69 & 0.77 & 0.93 & 0.57 & 0.74 \\ K-Nearest Neighbors & 0.85 & 0.97 & 0.75 & 0.77 & 0.96 & 0.96 & 0.95 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of each model on validation and test data, where stroke patients are classified as positive cases. healthy controls while walking. Among the seven models, the RF demonstrated the highest performance, and the confusion matrix showed that the RF model accurately classified all healthy and abnormal cases. This indicates that the model generalizes well to both the training and actual data. The muscle with the highest feature importance was the mean value of the VMM. Additionally, the maximum and mean values of the RFM emerged as important variables, highlighting the significance of EMG measurements and features derived from knee extensors among the various leg muscles for stroke prediction. This suggests that specific muscle EMG signals can be used efficiently to assess the likelihood of stroke in the presence of suspected warning signs or in early stages. The results of the predictive models using the training data in this study are supported by previous studies that identified quadriceps weakness as a key clinical symptom in patients [3; 65; 66; 67]. This study emphasizes the importance of the VMM in the quadriceps as a significant predictive factor. Following the VMM, it was found that the RFM and GCM were the significant predictors. This finding is also consistent with clinical observations and studies regarding the impact of stroke on these specific muscle groups. The RFM, a key muscle in the quadriceps group along with the VMM, plays a critical role in gait, particularly during the weight acceptance phase [40]. This reflects its high importance in our model. Whereas the GCM is essential for ankle plantar flexion and overall gait dynamics during the propulsion phase [40].