Parkinson's Disease Identification through Deep Optimum-Path Forest Clustering

Abstract

Approximately 50,000 to 60,000 new cases of Parkinson's disease (PD) are diagnosed yearly. Despite being non-lethal, PD shortens life expectancy of the ones affected with such disease. As such, researchers from different fields of study have put great effort in order to develop methods aiming the identification of PD in its early stages. This work uses handwriting dynamics data acquired by a series of tasks and proposes the application of a deep-driven graph-based clustering algorithm known as Optimum-Path Forest to learn a dictionary-like representation of each individual in order to automatic identify Parkinson's disease. Experimental results have shown promising results, with results comparable to some state-of-the-art approaches in the literature.