Abstract This study aims to investigate the effectiveness of various ML models—Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), and Decision Tree (DT)—in predicting walking recovery after TCSCI. The goal is to develop a robust, personalized predictive model using demographics, injury characteristics, peripheral inflammatory blood biomarkers, and neuroimaging factors metrics.
