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Diagnosis of Parkinson's disease using evolutionary algorithms

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Diagnosis of Parkinson's disease using evolutionary algorithms. / Smith, Stephen L.; Gaughan, Patrick; Halliday, David M.; Ju, Quan; Aly, Nabil M.; Playfer, Jeremy R.

In: Journal of Genetic Programming and Evolvable Machine, Vol. 8, No. 4, 12.2007, p. 433-447.

Research output: Contribution to journalArticlepeer-review

Harvard

Smith, SL, Gaughan, P, Halliday, DM, Ju, Q, Aly, NM & Playfer, JR 2007, 'Diagnosis of Parkinson's disease using evolutionary algorithms', Journal of Genetic Programming and Evolvable Machine, vol. 8, no. 4, pp. 433-447. https://doi.org/10.1007/s10710-007-9043-9

APA

Smith, S. L., Gaughan, P., Halliday, D. M., Ju, Q., Aly, N. M., & Playfer, J. R. (2007). Diagnosis of Parkinson's disease using evolutionary algorithms. Journal of Genetic Programming and Evolvable Machine, 8(4), 433-447. https://doi.org/10.1007/s10710-007-9043-9

Vancouver

Smith SL, Gaughan P, Halliday DM, Ju Q, Aly NM, Playfer JR. Diagnosis of Parkinson's disease using evolutionary algorithms. Journal of Genetic Programming and Evolvable Machine. 2007 Dec;8(4):433-447. https://doi.org/10.1007/s10710-007-9043-9

Author

Smith, Stephen L. ; Gaughan, Patrick ; Halliday, David M. ; Ju, Quan ; Aly, Nabil M. ; Playfer, Jeremy R. / Diagnosis of Parkinson's disease using evolutionary algorithms. In: Journal of Genetic Programming and Evolvable Machine. 2007 ; Vol. 8, No. 4. pp. 433-447.

Bibtex - Download

@article{b30019cf0d68413cb81fd563466328c2,
title = "Diagnosis of Parkinson's disease using evolutionary algorithms",
abstract = "This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson's symptom bradykinesia. Results are presented for 12 patients with Parkinson's disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous {"}leave one out{"} strategy was also applied to the test data with encouraging results.",
keywords = "Parkinson's disease, evolutionary algorithms, cartesian genetic programing, IMPLICIT CONTEXT",
author = "Smith, {Stephen L.} and Patrick Gaughan and Halliday, {David M.} and Quan Ju and Aly, {Nabil M.} and Playfer, {Jeremy R.}",
year = "2007",
month = dec,
doi = "10.1007/s10710-007-9043-9",
language = "English",
volume = "8",
pages = "433--447",
journal = "Journal of Genetic Programming and Evolvable Machine",
issn = "1389-2576",
publisher = "Springer New York",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Diagnosis of Parkinson's disease using evolutionary algorithms

AU - Smith, Stephen L.

AU - Gaughan, Patrick

AU - Halliday, David M.

AU - Ju, Quan

AU - Aly, Nabil M.

AU - Playfer, Jeremy R.

PY - 2007/12

Y1 - 2007/12

N2 - This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson's symptom bradykinesia. Results are presented for 12 patients with Parkinson's disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous "leave one out" strategy was also applied to the test data with encouraging results.

AB - This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson's symptom bradykinesia. Results are presented for 12 patients with Parkinson's disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous "leave one out" strategy was also applied to the test data with encouraging results.

KW - Parkinson's disease

KW - evolutionary algorithms

KW - cartesian genetic programing

KW - IMPLICIT CONTEXT

U2 - 10.1007/s10710-007-9043-9

DO - 10.1007/s10710-007-9043-9

M3 - Article

VL - 8

SP - 433

EP - 447

JO - Journal of Genetic Programming and Evolvable Machine

JF - Journal of Genetic Programming and Evolvable Machine

SN - 1389-2576

IS - 4

ER -