Using Explainable AI to Identify Disease-Relevant and Deep Brain Stimulation Treatment-Sensitive Gait Features in Parkinson's Disease

Gait impairment is a characteristic motor deficit of Parkinsons disease (PD) and a critical but insufficiently understood target of deep brain stimulation (DBS). Identifying robust gait biomarkers that capture both disease-related deficits and stimulation-induced improvements remains a major challenge. In this study, we analyzed 35 spatiotemporal gait parameters from individuals with PD before and after subthalamic DBS, alongside age-matched healthy controls, during continuous walking. Multiple machine learning classifiers were evaluated, with XGBoost achieving the best discrimination between groups. To ensure interpretability, we employed grouped SHapley Additive exPlanations (SHAP), which ranked feature importance while mitigating redundancy from correlated parameters. Feature selection consistently emphasized step width variability, step width asymmetry, bilateral interlimb coordination, and the anteroposterior margin of stability. Importantly, a compact set of five overlapping features after selection not only distinguished PD from healthy gait but also shifted toward healthy ranges after DBS. Unlike conventional mean-based metrics, the selected characteristics provided robust markers of both pathology and treatment response. Our findings demonstrate that explainable AI can identify physiologically grounded gait features that serve as biomarkers for both PD severity and DBS responsiveness, supporting more precise evaluation of neuromodulation outcomes and individualized patient management.