Parkinson's disease is a progressive neurodegenerative disorder. The biggest risk factor for developing Parkinson's disease is age and so prevalence is increasing in countries where the average age of the population is rising. Cognitive problems are common in Parkinson's disease and identifying those with the condition who are most at risk of developing such issues is an important area of research. In this work, we explore the potential for using objective, automated methods based around a simple figure copying exercise administered on a graphics tablet to people with Parkinson's disease. In particular, we use a multi-objective evolutionary algorithm to explore a space of regression models, where each model represents a combination of features extracted from a patient's digitised drawing. The objectives are to accurately predict clinical measures of the patient's motor and cognitive deficit. Our results show that both of these can be predicted, to a degree, and that certain sub-sets of features are particularly relevant in each case.

Item Type:	Refereed Conference Paper
Keywords:	Parkinson's, diagnostics, movement analysis, artificial intelligence
Research Division:	Biomedical and Clinical Sciences
Research Group:	Neurosciences
Research Field:	Neurology and neuromuscular diseases
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Diagnosis of human diseases and conditions
UTAS Author:	Alty, JE (Associate Professor Jane Alty)
ID Code:	144186
Year Published:	2016
Deposited By:	Wicking Dementia Research and Education Centre
Deposited On:	2021-04-27
Last Modified:	2022-09-08
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