Building Classifiers for Parkinson's Disease Using New Eye Tribe Tracking Method

Parkinson Disease (PD) is the second major neurodegenerative disease, which causes severe complications for patients’ daily life. PD remains unspecified in many aspects including best treatment, prediction of its progression and precise diagnosis. In our study we have built machine learning (ML) models, which address some of those issues by helping to improve symptom evaluation precision by using advanced biomarkers such as fast eye movements. We have built and compared model accuracy relaying on data from two systems for recording eye movements: one is saccadometer (Ober Consulting), and another is based on the Eye Tribe (ET1000). We have reached 85% accuracy in prediction of neurologic attributes based on ET and 82% accuracy with saccadometer with help of rough set theory. The purpose of this study was to compare ET with clinically approved eye movement measurements saccadometer of Ober. We have demonstrated in 8 PD patients that both systems gave comparable results based on neurological and eye movement measurements attributes.