Study uses machine learning to identify posturographic features in Parkinson's patients who fall.
problem Identifying patients at risk of falling in Parkinson's syndrome.
method Employed ts-AUC, a non-parametric multivariate two-sample test, to analyze posturographic features.
result Significant differences in antero-posterior movements and posturographic area between fallers and non-fallers identified.