University Of Tasmania

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Exploring diagnostic models of Parkinson’s disease with multi-objective regression

conference contribution
posted on 2023-05-23, 15:00 authored by Vallejo, M, Cosgrove, J, Jane AltyJane Alty, Jamieson, S, Smith, SL, Corne, DW, Lones, MA
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.


Publication title

2016 IEEE Symposium Series on Computational Intelligence (SSCI) - Proceedings






Wicking Dementia Research Education Centre



Place of publication

New York, United States

Event title

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Event Venue

Athens, Greece

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2016 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Diagnosis of human diseases and conditions; Artificial intelligence