Analysis, Identification and Prediction of Parkinson's disease sub-types
  and progression through Machine Learning
Ashwin Ram
Parkinson's disease (PD) is a prevalent neurodegenerative disorder with
varying patient trajectories, yet little is understood about the underlying
causes and symptom progression. The Parkinson's Progression Markers Initiative
(PPMI) has collected comprehensive longitudinal data from diverse patient
cohorts to identify biomarkers and aid in the development of interventions.
Despite over 110 machine learning studies using the PPMI database, the majority
have focused on supervised models for diagnosis prediction, which has limited
impact on understanding patient variability and progression. This paper
addresses this gap by combining supervised and unsupervised machine learning
methods to identify subtypes that accurately predict disease progression in
Parkinson's patients. Building upon previous work, we replicate and extend the
study by integrating unsupervised patient clustering and prediction of present
and future symptoms using 5 additional years of longitudinal data from the
Progressive Parkinson's Markers Initiative (PPMI) database. Our findings
demonstrate accurate prediction of disease trajectories and symptoms at
baseline, offering valuable insights into patient heterogeneity and the
potential for personalized interventions. The integration of supervised and
unsupervised models presents a promising avenue for uncovering latent subgroups
and understanding the complexity of Parkinson's disease progression.