A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients

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

doi:10.1145/2908961.2931731

A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.

Original language	English
Title of host publication	GECCO 2016 Companion
Subtitle of host publication	Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Publisher	Association for Computing Machinery
Pages	1369-1376
Number of pages	8
ISBN (Print)	9781450343237
DOIs	https://doi.org/10.1145/2908961.2931731
Publication status	Published - 2016
Event	Genetic and Evolutionary Computation Conference 2016 - Denver, United States Duration: 20 Jul 2016 → 24 Jul 2016

Conference

Conference	Genetic and Evolutionary Computation Conference 2016
Abbreviated title	GECCO 2016
Country/Territory	United States
City	Denver
Period	20/07/16 → 24/07/16

Keywords

Multi-objective evolutionary algorithms
Parkinson's disease
Polynomial regression
Predictive modelling

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10.1145/2908961.2931731

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Vallejo, M., Cosgrove, J., Alty, J. E., Jamieson, S., Smith, S. L., Corne, D. W., & Lones, M. A. (2016). A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients. In GECCO 2016 Companion: Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 1369-1376). Association for Computing Machinery. https://doi.org/10.1145/2908961.2931731

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title = "A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients",

abstract = "Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.",

keywords = "Multi-objective evolutionary algorithms, Parkinson's disease, Polynomial regression, Predictive modelling",

author = "Marta Vallejo and Jeremy Cosgrove and Alty, {Jane E.} and Stuart Jamieson and Smith, {Stephen L.} and Corne, {David W.} and Lones, {Michael Adam}",

year = "2016",

doi = "10.1145/2908961.2931731",

language = "English",

isbn = "9781450343237",

pages = "1369--1376",

booktitle = "GECCO 2016 Companion",

publisher = "Association for Computing Machinery",

address = "United States",

note = "Genetic and Evolutionary Computation Conference 2016, GECCO 2016 ; Conference date: 20-07-2016 Through 24-07-2016",

}

Vallejo, M, Cosgrove, J, Alty, JE, Jamieson, S, Smith, SL, Corne, DW & Lones, MA 2016, A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients. in GECCO 2016 Companion: Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp. 1369-1376, Genetic and Evolutionary Computation Conference 2016, Denver, United States, 20/07/16. https://doi.org/10.1145/2908961.2931731

A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients. / Vallejo, Marta; Cosgrove, Jeremy; Alty, Jane E. et al.
GECCO 2016 Companion: Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2016. p. 1369-1376.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients

AU - Vallejo, Marta

AU - Cosgrove, Jeremy

AU - Alty, Jane E.

AU - Jamieson, Stuart

AU - Smith, Stephen L.

AU - Corne, David W.

AU - Lones, Michael Adam

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AB - Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.

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A multi-objective approach to predicting motor and cognitive deficit in Parkinson's disease patients

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