Predicting Parkinson's Disease Progression: Analyzing Prodromal Stages Through Machine Learning

This study explores prodromal Parkinson's Disease (PD) by leveraging data from the Parkinson's Progression Markers Initiative (PPMI). The main goal was to discriminate between prodromals that phenoconverted to PD in 7 years to those that did not. Through feature selection, the system identified key first visit predictors of PD phenoconversion, encompassing demographic, clinical, and structural magnetic resonance imaging (MRI) data. Employing seven machine learning algorithms in standard and balanced forms, we find Support Vector Machine (balanced) as most effective for demographic and clinical data, and Logistic Regression (balanced) when adding thicknesses and volumes of MRI data. The metrics were improve in the second case (AUC ROC of 0.84). Significant predictors include olfactory dysfunction, motor symptoms, psychomotor speed, and third ventricle dilation.