Team Members: Dennis Salinas, Veronica Mata, Sherin Thomas, Sayli Shivalkar, Alejandro Cruz
Cognitive impairment is a common symptom of Parkinson’s disease (PD), with patients experiencing varying levels of severity depending on the stage and progression of the disease. The goal of this project was to predict whether a patient with PD and alpha-synuclein pathology (a biological marker associated with the disease) would experience a rapid or normal cognitive decline, using data from the Parkinson’s Progression Markers Initiative database. Students analyzed cognitive assessment scores over time and fitted data to a linear regression model to classify each patient as rapid or steady decliners based on their rate of deterioration. They then used these labels to train a classification machine learning model to predict which patients would fall into which categories. The team gained significant insights into key predictors of rapid cognitive decline for patients with PD, including clinical, biological, and brain imaging measures, providing insights that could help identify future at-risk patients early in their disease progression.