Sequential Feature Selection and Adaptive Ensemble Classification for Parkinson’s Disease Detection

Abstract

Parkinson’s Disease (PD) significantly affects motor functions, with gait disturbances being a signal symptom. This study leverages biomedical information fusion to enhance PD diagnosis through gait analysis. Using a 6-axis inertial measurement unit (IMU) mounted on insoles, we collected a comprehensive dataset comprising over 2,000 samples and 164 gait features from PD patients and healthy controls. A novel sequential feature selection strategy was developed, reducing redundancy and identifying 41 critical gait metrics, enabling efficient and interpretable analysis. An adaptive voting ensemble model was proposed, effectively addressing patient heterogeneity and achieving superior predictive performance with an AUC of 0.9055. This method has the potential to optimize wearable systems to facilitate real-time monitoring and early interventions for PD.