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

Marta Vallejo, Stuart Jamieson, Jeremy Cosgrove, Stephen L. Smith, Michael Adam Lones, Jane E. Alty, David Corne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence
PublisherIEEE
ISBN (Electronic)978-1-5090-4240-1
DOIs
Publication statusPublished - Dec 2016
Event2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health
Abbreviated titleIEEE SSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

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Vallejo, M., Jamieson, S., Cosgrove, J., Smith, S. L., Lones, M. A., Alty, J. E., & Corne, D. (2016). Exploring diagnostic models of Parkinson's disease with multi-objective regression. In 2016 IEEE Symposium Series on Computational Intelligence IEEE. https://doi.org/10.1109/SSCI.2016.7849884
Vallejo, Marta ; Jamieson, Stuart ; Cosgrove, Jeremy ; Smith, Stephen L. ; Lones, Michael Adam ; Alty, Jane E. ; Corne, David. / Exploring diagnostic models of Parkinson's disease with multi-objective regression. 2016 IEEE Symposium Series on Computational Intelligence. IEEE, 2016.
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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.",
author = "Marta Vallejo and Stuart Jamieson and Jeremy Cosgrove and Smith, {Stephen L.} and Lones, {Michael Adam} and Alty, {Jane E.} and David Corne",
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Vallejo, M, Jamieson, S, Cosgrove, J, Smith, SL, Lones, MA, Alty, JE & Corne, D 2016, Exploring diagnostic models of Parkinson's disease with multi-objective regression. in 2016 IEEE Symposium Series on Computational Intelligence. IEEE, 2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7849884

Exploring diagnostic models of Parkinson's disease with multi-objective regression. / Vallejo, Marta; Jamieson, Stuart; Cosgrove, Jeremy; Smith, Stephen L.; Lones, Michael Adam; Alty, Jane E.; Corne, David.

2016 IEEE Symposium Series on Computational Intelligence. IEEE, 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Vallejo M, Jamieson S, Cosgrove J, Smith SL, Lones MA, Alty JE et al. Exploring diagnostic models of Parkinson's disease with multi-objective regression. In 2016 IEEE Symposium Series on Computational Intelligence. IEEE. 2016 https://doi.org/10.1109/SSCI.2016.7849884