Abstract
Incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome are major neurological disorders that significantly reduce patients' quality of life, primarily due to impaired motor function and gait instability. Although conventional neurological assessments and imaging techniques are widely used for diagnosis, they are limited by temporal constraints and physical accessibility. This study explores the integration of machine learning and 3D motion capture gait data for effective classification of these conditions. Gait data from 214 patients were analyzed, and key features were identified using recursive feature elimination. Machine learning models, including support vector machine, random forest, and XGBoost, were trained and validated. The XGBoost model achieved the highest accuracy (74.42%) and F1-score (74.27%), with age, cadence, and double support emerging as the most influential features. Sex-based differences revealed that males exhibited greater dynamic gait variables, while females showed higher stability-oriented metrics. Age-based analysis indicated significant gait changes after 60 years, highlighting the role of stability-related features. These findings demonstrate the potential of integrating 3D motion capture and machine learning as a scalable, noninvasive diagnostic tool. By detecting subtle gait variations, this approach can aid in early diagnosis and personalized treatment planning for individuals with neurological impairments.