Preface

This notebook provides the code of the analysis described in the manuscript “Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson’s Disease from Other Forms of Parkinsonism”. The work involves using machine learning and movement data to distinguish idiopathic PD from non-PD parkinsonism. Wearable sensor data were collected from a cohort of 260 individuals diagnosed with PD and 18 participants who were diagnosed with other forms of parkinsonism. Each participant performed five motor tasks, including a 32-foot walk involving walking back and forth four times with 180 degree turns between segments, standing with eyes open, standing with eyes closed, two trials of the Timed Up & Go test (TUG), and two trials of the cognitive TUG (cogTUG). We used a three-fold cross-validation technique to create training and test splits for our model training using stratified sampling. This process was repeated five times with different random seeds to improve robustness. The final class for each participant was determined by the majority vote of the predicted classes from the five replicates. Within each fold, an Mutual Information based feature selection for EasyEnsemble (MIEE) model was trained on the training set and tested on the corresponding test set to prevent data leakage and ensure generalization.