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

Prediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability