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 |
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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 | |
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 |
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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