- author = "Stuart E. Lacy",
- title = "Modeling Movement Disorders in {Parkinson's} Disease using Computational Intelligence",
- school = "Electronics, University of York",
- year = "2015",
- address = "UK",
- month = dec,
- keywords = "genetic algorithms, genetic programming, ESN, Parkinsons Disease, computational intelligence, evolutionary algorithms, time series analysis",
-
URL = "
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685246?",
-
URL = "http://etheses.whiterose.ac.uk/12964/",
-
URL = "
https://etheses.whiterose.ac.uk/12964/1/StuartLacy_finalthesis.pdf",
- size = "248 pages",
-
abstract = "Parkinson's is the second most common
neurodegenerative disease after Alzheimer's Disease and
affects 127000 people in the UK alone. Providing the
most appropriate treatment pathway can prove
challenging owing to the difficulty in obtaining an
accurate diagnosis; due to its similarity in symptoms
with other neurodegenerative diseases, it is estimated
that in the United Kingdom around 24percent of cases
are misdiagnosed by general neurologists. A means of
providing an accurate and early diagnosis of
Parkinson's Disease would thereby enable a more
effective management of the disease, increased quality
of life for patients, and reduce costs to the
healthcare system.
The work described in this thesis details progress towards this goal by modeling movement disorders in the form of positional data recorded from simple movement tasks, building towards a fully objective diagnostic system without requiring any specialist domain knowledge. This is accomplished by modeling established movement disorder markers using Evolutionary Algorithms to train ensembles, before implementing feature design strategies with both Genetic Programming and Echo State Networks.
The findings of this study make an important contribution to the area of data mining, including: the demonstration that Computational Intelligence-based feature design strategies can be competitive to conventional models using features extracted with expert domain knowledge; a thorough survey of evolutionary ensemble research; and the development of a novel evolutionary ensemble approach comprising traditional single objective Evolutionary Algorithm. Furthermore, an extension to a Genetic Programming feature design strategy for periodic time series is detailed, in addition to demonstrating that Echo State Networks can be directly applied to time series classification as a feature design method. This research was carried out in the context of building an applied diagnostic aid and required developing models with means of indicating the most discriminatory aspects of the sequence data, thereby facilitating inference of the precise mechanics of movement disorders to clinical neurologists.",
-
notes = "HUMIE winner.
Also known as \cite{wreo12964}
ISNI: 0000 0004 5924 3488
Steve Smith and Mic Lones",
