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 proceedingConference contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publicationGECCO 2016 Companion
Subtitle of host publicationProceedings of the 2016 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781450343237
Publication statusPublished - 2016
EventGenetic and Evolutionary Computation Conference 2016 - Denver, United States
Duration: 20 Jul 201624 Jul 2016


ConferenceGenetic and Evolutionary Computation Conference 2016
Abbreviated titleGECCO 2016
Country/TerritoryUnited States


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


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