Background/Objective: Muscle synergy analysis based on machine learning has significantly advanced our understanding of the mechanisms underlying the central nervous system motor control of gait and has identified abnormal gait synergies in stroke patients through various analytical approaches. However, discrepancies in experimental conditions and computational methods have limited the clinical application of these findings. This review seeks to integrate the results of existing studies on the features of muscle synergies in stroke-related gait abnormalities and provide clinical and research insights into gait rehabilitation. Methods: A systematic search of Web of Science, PubMed, and Scopus was conducted, yielding 10 full-text articles for inclusion. Results: By comprehensively reviewing the consistencies and differences in the study outcomes, we emphasize the need to segment the gait cycle into specific phases (e.g., weight acceptance, push-off, foot clearance, and leg deceleration) during the treatment process of gait rehabilitation and to develop rehabilitation protocols aimed at restoring normal synergy patterns in each gait phase and fractionating reduced synergies. Conclusions: Future research should focus on validating these protocols to improve clinical outcomes and introducing indicators to assess abnormalities in the temporal features of muscle synergies.

Colorectal cancer (CRC) has a relatively high five-year survival rate compared to other cancers; however, this rate drops significantly in patients with malignant CRC. One critical factor in palliative care decision-making is the ability to accurately predict patient survival, with the six-month survival period commonly used as a threshold. In this study, we evaluated the performance of five machine learning models—logistic regression, decision tree, random forest, multilayer perceptron, and extreme gradient boosting (XGBoost)—in predicting six-month survival for patients with malignant CRC using a publicly available synthetic dataset containing 11,774 samples and 51 features. The models were trained and validated using five-fold cross-validation, and the synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Among the models, XGBoost demonstrated the highest performance, achieving 95% accuracy, precision, recall, and F1-score, along with 90% specificity. Feature importance analysis identified smoking status and surgical history as key factors influencing model predictions. These findings highlight the potential of tree-based machine learning models in supporting timely and informed palliative care decisions, while also providing insights into handling data imbalance and optimizing model parameters in survival prediction tasks.