Author Date

2020-03-06

Degree Name

Department

Computer Science

College

Physical and Mathematical Sciences

Defense Date

2020-02-21

Publication Date

2020-03-06

First Faculty Advisor

Mark Clement

First Faculty Reader

Quinn Snell

Honors Coordinator

Seth Holladay

Keywords

machine learning, random forest, decision tree, Parkinson's disease

Abstract

Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.