Advancing Parkinson’s disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics

Ensemble-based methods effectively capture the complex, non-linear relationship between movement characteristics and PD diagnosis. Comprehensive feature extraction frameworks incorporating multiple movement dimensions significantly enhance detection accuracy. The asymmetric feature influence patterns for positive versus negative predictions align with clinical understanding of PD as a disorder characterized by altered movement complexity and variability. These findings provide a foundation for developing accurate, interpretable wearable monitoring systems for Parkinson's disease detection and management.