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Application of classification for figure copying test in Parkinson's disease diagnosis by using cartesian genetic programming

conference contribution
posted on 2023-05-23, 14:59 authored by Xia, T, Cosgrove, J, Jane AltyJane Alty, Jamieson, S, Smith, J
Previous studies have proposed an objective non-invasive approach to assist diagnosing neurological diseases such as Alzheimer and Parkinson's diseases by asking patients to perform certain drawing tasks against certain figure. However, the approach of rating those drawing test results is still very subjective by relying on manual measurements. By extracting features of the drawn figure from the raw data, which is generated from the digitized tablet that patients can draw on, we can use supervised learning to train the evolutionary algorithm with those extracted data, and therefore evolves an automated classifier to analyse and classify those drawing accurately. Cartesian Genetic Programming (CGP) is an improved version of conventional Genetic Programming (GP). As GP adapts the tree structure, redundancy issue exists as the tree develops more nodes with the evolution of the GP by mutation and crossover. CGP addresses this issue by using fixed number of nodes and arities, evolves by using mutation only. The outcome of this research is a highly efficient, accurate, automated classifier that can not only classify clinical drawing test results, which can provide up to 80% accuracy, but also assisting clinicians and medical experts to investigate how those features are used by the algorithm and how each component can impact patient's cognitive function.

CCS CONCEPTS

• Computing methodologies ~ Supervised learning by classification • Applied computing~Health care information systems

History

Pagination

1855–1863

ISBN

978-1-4503- 6748-6

Department/School

Wicking Dementia Research Education Centre

Publisher

Association for Computing Machinery

Place of publication

Czech Republic

Event title

GECCO '19 Companion

Event Venue

Prague, Czech Republic

Date of Event (Start Date)

2019-07-13

Date of Event (End Date)

2019-07-17

Rights statement

Copyright 2019 Association for Computing Machinery

Repository Status

  • Restricted

Socio-economic Objectives

Artificial intelligence

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    University Of Tasmania

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