Diagnosis of Neurological Disease using Evolutionary Classifiers
- Members: Steve Smith, Michael Lones, David Halliday
- Start Date: June 2008
This research uses novel evolutionary algorithms to induce classifiers capable of recognising the symptoms of neurological diseases such as Parkinson's and Alzheimer's. More information can be found in the following publications:
Smith, S. L. and Lones, M. A. “Implicit Context Representation Cartesian Genetic Programming for the Assessment of Visuo-Spatial Ability,” in Proc. 2009 IEEE Congress on Evolutionary Computation, May 2009.
Smith, S.L. et. al. “Diagnosis of Parkinson's Disease using Evolutionary Algorithms,” Genetic Programming and Evolvable Machines, In press, 2007.
Aly, N. M., Playfer, J. R., Smith, S. L. and Halliday, D. M., “A novel computer-based technique for the assessment of tremor in Parkinson's disease,” Age and Ageing, doi:10.1093, June 2007.
Allen, D.P., Playfer, J.R., Aly, N.M.,. Duffey, P., A. Heald, A., Smith, S.L. and Halliday, D.M., "On the use of low-cost computer peripherals for the assessment of motor dysfunction in Parkinson’s disease" IEEE Trans. Neural Systems & Rehabilitation Engineering, Vol.15, No.2, 286-294, 2007.
Smith, S.L. et. al. “The Application of Evolutionary Algorithms towards the Diagnosis of Parkinson's Disease,” GECCO Workshop on Medical Applications of GEC, Seattle, July, 2006.
Allen, D.P., Halliday, D.M., Smith, S.L., Playfer, J. & Aly, N. "A clinical diagnostic tool for the assessment of bradykinesia in Parkinson’s disease using using low cost computer peripherals," Institute of Physics and Engineering in Medicine, Annual Scientific Meeting, 34, 2005.