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Parkinson's disease (PD) is a prevalent neurodegenerative disorder withvarying patient trajectories, yet little is understood about the underlyingcauses and symptom progression. The Parkinson's Progression Markers Initiative(PPMI) has collected comprehensive longitudinal data from diverse patientcohorts to identify biomarkers and aid in the development of interventions.Despite over 110 machine learning studies using the PPMI database, the majorityhave focused on supervised models for diagnosis prediction, which has limitedimpact on understanding patient variability and progression. This paperaddresses this gap by combining supervised and unsupervised machine learningmethods to identify subtypes that accurately predict disease progression inParkinson's patients. Building upon previous work, we replicate and extend thestudy by integrating unsupervised patient clustering and prediction of presentand future symptoms using 5 additional years of longitudinal data from theProgressive Parkinson's Markers Initiative (PPMI) database. Our findingsdemonstrate accurate prediction of disease trajectories and symptoms atbaseline, offering valuable insights into patient heterogeneity and thepotential for personalized interventions. The integration of supervised andunsupervised models presents a promising avenue for uncovering latent subgroupsand understanding the complexity of Parkinson's disease progression.

Analysis, Identification and Prediction of Parkinson's disease sub-types and progression through Machine Learning