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

doi:10.1109/SSCI.2016.7849884

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 proceeding › Conference contribution

2 Citations (Scopus)

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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 language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence
Publisher	IEEE
ISBN (Electronic)	978-1-5090-4240-1
DOIs	https://doi.org/10.1109/SSCI.2016.7849884
Publication status	Published - Dec 2016
Event	2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016

Conference

Conference	2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health
Abbreviated title	IEEE SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16

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@inproceedings{a0113a8372844ce5b9f943e0f6ed3a66,

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",

year = "2016",

month = dec,

doi = "10.1109/SSCI.2016.7849884",

language = "English",

booktitle = "2016 IEEE Symposium Series on Computational Intelligence",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health, IEEE SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

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

TY - GEN

T1 - Exploring diagnostic models of Parkinson's disease with multi-objective regression

AU - Vallejo, Marta

AU - Jamieson, Stuart

AU - Cosgrove, Jeremy

AU - Smith, Stephen L.

AU - Lones, Michael Adam

AU - Alty, Jane E.

AU - Corne, David

PY - 2016/12

Y1 - 2016/12

N2 - 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.

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.

U2 - 10.1109/SSCI.2016.7849884

DO - 10.1109/SSCI.2016.7849884

M3 - Conference contribution

BT - 2016 IEEE Symposium Series on Computational Intelligence

PB - IEEE

T2 - 2016 IEEE Symposium on Computational Intelligence in Healthcare and e-health

Y2 - 6 December 2016 through 9 December 2016

ER -

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

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