Title
Exploring diagnostic models of Parkinson's disease with multi-objective regression
Abstract
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.
Year
DOI
Venue
2016
10.1109/SSCI.2016.7849884
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywords
Field
DocType
Parkinson's disease diagnostic model,multiobjective regression,progressive neurodegenerative disorder,cognitive problem,objective automated method,graphics tablet,multiobjective evolutionary algorithm,patient digitised drawing,clinical measures,patient motor deficit,patient cognitive deficit
Population,Graphics tablet,Cognitive deficit,Disease,Parkinson's disease,Regression analysis,Artificial intelligence,Physical medicine and rehabilitation,Cognition,Medicine,Risk factor
Conference
ISBN
Citations 
PageRank 
978-1-5090-4241-8
0
0.34
References 
Authors
7
7
Name
Order
Citations
PageRank
Marta Vallejo1122.96
D. R. Stuart Jamieson221.72
Jeremy Cosgrove342.81
Stephen L Smith4116383.01
Michael A. Lones516820.42
Jane E. Alty6377.58
David W. Corne72161152.00