Using Handwriting Evaluation Software to Predict and Increase Diagnosis for Parkinson’s

Abstract

Around the world, there currently exists a problem when it comes to the diagnosis of Parkinson’s disease (PD). Unfortunately, nearly half of all Americans who have PD remain undiagnosed, which is problematic when one considers the implications of such ignorance. People who continue to be undiagnosed do not have access to special treatments, therapies, and medications that would help alleviate the symptoms of PD and decrease the burden of it altogether. Fortunately, amidst recent technological advancements in computing and the contemporary paradigm shift to using handwriting as a diagnosis method for PD, a shimmer of hope reveals itself. By using a machine learning software program that predicts a user’s likelihood of having PD through their handwriting alone, people might feel more inclined to seek a formal evaluation for the disease. Since it is rather inexpensive, based on concrete, quantitative kinematics of an individual’s handwriting, and holds legitimacy due to the existence of similar evaluation programs, the software could help increase the amount of people that seek a formal PD evaluation and diagnosis.