You are in : Home » Results & submissions » Select UOA » 13 - Electrical and Electronic Engineering, Metallurgy and Materials » View submission: University of York » Outputs » Detail

Output details

13 - Electrical and Electronic Engineering, Metallurgy and Materials

University of York

Return to search Previous output Next output

Output 0 of 0 in the submission

Article title

Evolving classifiers to recognise the movement characteristics of Parkinson’s Disease patients

Type

D - Journal article

DOI

10.1109/TEVC.2013.2281532

Title of journal

IEEE Transactions on Evolutionary Computation

Article number

Volume number

n/a

Issue number

First page of article

n/a

ISSN of journal

1089-778X

Year of publication

2013

URL

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6600775

Number of additional authors

Additional information

The work advances medical diagnosis. The result of an EPSRC funded project (EP/F060041/1), in collaboration with Leeds General Infirmary and the University of California San Francisco Medical Center, two novel evolutionary algorithms in an ensemble classifier are used to diagnose Parkinson’s disease with a diagnostic accuracy of 95% - comparable to that demonstrated by expert clinicians and considerably higher than those achieved in primary and community care.

Interdisciplinary

Cross-referral requested

Research group

B - Intelligent Systems

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract