A Machine Learning Model With Temporal Validation for Predicting Walking Ability in Nontraumatic Spinal Cord Injury

Abstract

BACKGROUND: Walking ability is the priority for spinal cord injury (SCI) patients. Nontraumatic SCI (NTSCI) is increasing with aging populations, yet its prognosis remains understudied. Early predictionF using admission clinical indicators could facilitate decision-making and rehabilitation planning. OBJECTIVE: To preliminarily develop and validate a machine learning model using early admission clinical indicators for predicting post-rehabilitation walking ability probability (with a classification threshold of 0.5) in patients with NTSCI. METHODS: This retrospective study included model development and temporal external validation. We enrolled 148 NTSCI patients, randomly allocating them into training (80%) and internal test (20%) sets. Four feature sets were constructed from objective clinical measures. Nine algorithms were compared; Gradient Boosting Decision Tree (GBDT) was selected based on AUC, recall, and F2-score, followed by hyperparameter tuning. The final model was validated independently on a cohort of 37 NTSCI patients (July 2025 to May 2026). Ethical approval (M202616) and consent waiver obtained; retrospectively registered (MR-11-26-021748); no patient involvement. RESULTS: The GBDT-based model outperformed all others. On internal testing, it achieved an AUC of 0.91 (95% CI: 0.77-1.00), recall 0.92 (95% CI: 0.71-1.00), and accuracy 0.90 (95% CI: 0.77-1.00). Key predictors included total lower limb muscle strength, age, and others. On temporal external validation, it showed encouraging performance with accuracy 0.81 (95% CI: 0.68-0.92) and recall 0.70 (95% CI: 0.40-1.00). CONCLUSION: This preliminary model may have potential for predicting walking ability in patients with NTSCI; however, these findings are exploratory and require confirmation in larger, multicenter cohorts before any clinical application can be considered.