Nathan Estrin

Bachelor's

Biomedical Science

Marquette University

- Designed a predictive model in R that quantifies the risk and rate of progression in patients with Parkinson's Disease aimed to provide valuable insights for early detection and personalized treatment - Analyzed exploratory visualization studies within the Michael J. Fox Research dataset of 50,000 participants, revealing a 32.5% prevalence of sleep disturbances, similar to diagnosed participants, in undiagnosed individuals, emphasizing the importance of early identification of non-motor symptoms - Utilized multiple linear regression models to predict age at diagnosis, revealing impactful results: Discovered higher caffeine intake correlated with a later onset of Parkinson's, indicating a potential protective effect (increase by about 3.3 years - Established a nuanced risk assessment model through CART analysis, highlighting key predictors like family history, age, and pesticide - Highlighted the de pendability of the logistic regression model in forecasting Parkinson's Disease progression, substantiated by an impressive