Diagnosis of Parkinson's disease using evolutionary algorithms

Stephen L. Smith, Patrick Gaughan, David M. Halliday, Quan Ju, Nabil M. Aly, Jeremy R. Playfer

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)433-447
Number of pages15
JournalJournal of Genetic Programming and Evolvable Machine
Issue number4
Publication statusPublished - Dec 2007


  • Parkinson's disease
  • evolutionary algorithms
  • cartesian genetic programing

Cite this