  Parkinson's disease (PD) is a common neurological disorder characterized by
gait impairment. PD has no cure, and an impediment to developing a treatment is
the lack of any accepted method to predict disease progression rate. The
primary aim of this study was to develop a model using clinical measures and
biomechanical measures of gait and postural stability to predict an
individual's PD progression over two years. Data from 160 PD subjects were
utilized. Machine learning models, including XGBoost and Feed Forward Neural
Networks, were developed using extensive model optimization and
cross-validation. The highest performing model was a neural network that used a
group of clinical measures, achieved a PPV of 71% in identifying fast
progressors, and explained a large portion (37%) of the variance in an
individual's progression rate on held-out test data. This demonstrates the
potential to predict individual PD progression rate and enrich trials by
analyzing clinical and biomechanical measures with machine learning.
