Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of individual progression rate in Parkinson's disease using
  clinical measures and biomechanical measures of gait and postural stability   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.

35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an