  At present, the vast majority of human subjects with neurological disease are
still diagnosed through in-person assessments and qualitative analysis of
patient data. In this paper, we propose to use Topological Data Analysis (TDA)
together with machine learning tools to automate the process of Parkinson's
disease classification and severity assessment. An automated, stable, and
accurate method to evaluate Parkinson's would be significant in streamlining
diagnoses of patients and providing families more time for corrective measures.
We propose a methodology which incorporates TDA into analyzing Parkinson's
disease postural shifts data through the representation of persistence images.
Studying the topology of a system has proven to be invariant to small changes
in data and has been shown to perform well in discrimination tasks. The
contributions of the paper are twofold. We propose a method to 1) classify
healthy patients from those afflicted by disease and 2) diagnose the severity
of disease. We explore the use of the proposed method in an application
involving a Parkinson's disease dataset comprised of healthy-elderly,
healthy-young and Parkinson's disease patients. Our code is available at
https://github.com/itsmeafra/Sublevel-Set-TDA.
