Abstract
INTRODUCTION: Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder, with subtle gait changes such as reduced vertical ground-reaction forces (VGRF) often preceding motor symptoms. These gait abnormalities, measurable via wearable VGRF sensors, offer a non-invasive means for early PD detection. However, current computational approaches often suffer from redundant features and class imbalance, limiting both accuracy and generalizability. METHODS: We propose CRISP (Correlation-filtered Recursive Feature Elimination and Integration of SMOTE Pipeline for Gait-Based Parkinson's Disease Screening), a lightweight multistage framework that sequentially applies correlation-based feature pruning, recursive feature elimination (RFE), and Synthetic Minority Oversampling Technique (SMOTE) based class balancing. To ensure clinically meaningful evaluation, a novel subject-wise protocol was also introduced that assigns one prediction per individual enhancing patient-level variability capture and better aligning with diagnostic workflows. Using 306 VGRF recordings (93 PD, 76 controls), five classifiers, i.e., k-Nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Gradient boosting (GB), and Extreme Gradient Boosting (XGBoost) were evaluated for both binary PD detection and multiclass severity grading. RESULTS: CRISP consistently improved performance across all models under 5-fold cross-validation. XGBoost achieved the highest performance, increasing subject-wise PD detection accuracy from 96.1 ± 0.8% to 98.3 ± 0.8%, and severity grading accuracy from 96.2 ± 0.7% to 99.3 ± 0.5%. CONCLUSION: CRISP is the first VGRF-based pipeline to combine correlation-filtered feature pruning, recursive feature elimination, and SMOTE to enhance PD detection performance, while also introducing a subject-wise evaluation protocol that captures patient-level variability for truly personalized diagnostics. These twin novelties deliver clinically significant gains and lay the foundation for real-time, on-device PD detection and severity monitoring.