research-article

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

Authors:

Jeremy Cosgrove,

Stuart Jamieson,

Stephen SmithAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1855 - 1863

https://doi.org/10.1145/3319619.3326822

Published: 13 July 2019 Publication History

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

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

Pages 1855 - 1863

Abstract
References

Abstract

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.

References

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Digital Library

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Cited By

Maurya PKushwaha APrakash O(2025)Medical Data Classification Using Genetic Programming: A Systematic Literature ReviewExpert Systems10.1111/exsy.7000742:3Online publication date: 5-Feb-2025
https://doi.org/10.1111/exsy.70007
Lavinas YHaut NPunch WBanzhaf WCussat-Blanc SLi XHandl J(2024)Dynamically Sampling biomedical Images For Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654202(515-518)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654202
Lavinas YHaut NPunch WBanzhaf WCussat-Blanc S(2024)Adaptive Sampling of Biomedical Images with Cartesian Genetic ProgrammingParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_16(256-272)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_16

Index Terms

Application of classification for figure copying test in Parkinson's disease diagnosis by using cartesian genetic programming
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

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Published: 13 July 2019

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GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Cited By

Maurya PKushwaha APrakash O(2025)Medical Data Classification Using Genetic Programming: A Systematic Literature ReviewExpert Systems10.1111/exsy.7000742:3Online publication date: 5-Feb-2025
https://doi.org/10.1111/exsy.70007
Lavinas YHaut NPunch WBanzhaf WCussat-Blanc SLi XHandl J(2024)Dynamically Sampling biomedical Images For Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654202(515-518)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654202
Lavinas YHaut NPunch WBanzhaf WCussat-Blanc S(2024)Adaptive Sampling of Biomedical Images with Cartesian Genetic ProgrammingParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_16(256-272)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_16

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