## 5 Discussion
Stroke can severely affect this muscle's function, leading to a decrease in gait speed. The feature importance of the GCM in our model corroborates the well-documented impact of stroke on lower limb function and emphasizes the importance of this muscle in maintaining effective mobility and balance. This finding highlights how machine learning models can effectively capture critical aspects of motor dysfunction associated with stroke, providing valuable insights for clinical assessments and rehabilitation strategies. This correlation not only validates the model's performance but also reinforces the clinical significance of these muscle groups in the context of stroke rehabilitation. Additionally, although beyond the primary scope of this study, feature importance analysis indicated higher values for females, which aligns with global statistics showing a higher stroke risk among women [13]. Furthermore, this study did not differentiate between the paralyzed and non-paralyzed legs of stroke patients, as both legs can experience kinematic damage in stroke patients [66]. In this study, the RF model exhibited the best performance with EMG-driven features, demonstrating its effectiveness in capturing the nonlinearity and feature interactions among the 64 features, as well as its robustness in preventing overfitting. Given that EMG data involves correlations between muscle activations, it is crucial to consider these interdependencies during the learning process. Models like NB, which assume feature independence, showed diminished performance. While models such as SVM and MLP are known for their ability to handle high-dimensional data, their performance is highly sensitive to hyperparameter tuning. As a result, this study alone cannot definitively conclude that RF surpasses SVM and MLP. Stroke is a major disease that significantly affects the quality of life and leads to severe sequelae in the elderly. Early detection and prompt intervention based on warning signs can be highly beneficial not only in improving outcomes but also in reducing healthcare and socioeconomic costs for the elderly [68]. A recent study reported several barriers to the clinical evaluation and application of EMG [69]. The results of this study demonstrate that predictive models can perform effectively using only the mean or maximum values of muscle activity of the knee extensors without requiring complex analysis of clinical significance or symptom testing. This approach addresses both the critical issue of computational efficiency in time-series data and the challenge of technological accessibility in clinical settings [70]. Rapid initial predictions using ML based on computationally efficient time-series data followed by precise evaluations are expected to alleviate bottlenecks in future medical diagnostic systems [71]. Moreover, because the simultaneous measurement of multiple muscles is not required, managing data quality related to noise and outliers can be managed more easily. Although various studies have proposed the use of ML modelsfor disease prediction, research that validates and examines the performance and impact of stroke prediction models based on the activation of multiple muscles while considering the complex joint degrees of freedom in the lower limbs, as in this study, is rare [72]. This study represents a pilot investigation into the feasibility of utilizing a limited set of muscle activity data for ML-based predictive models. To advance these models for clinical applications, conducting further research that incorporates a range of factors related to stroke patients is crucial, including the etiology of impairment, lesion localization, extent of recovery, and prognosis, by leveraging muscle activity data from specific muscle groups. Moreover, subsequent research should focus on enhancing not only the sensitivity of the models for stroke detection but also their specificity in distinguishing stroke from other diseases.