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

Xia, T, Cosgrove, J, Alty, J ORCID: 0000-0002-5456-8676, Jamieson, S and Smith, J 2019 , 'Application of classification for figure copying test in Parkinson's disease diagnosis by using cartesian genetic programming', in GECCO '19 Companion, Prague, Czech Republic 13–17 July , Association for Computing Machinery, Czech Republic, 1855–1863 , doi: 10.1145/3319619.3326822.

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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.CCS CONCEPTS• Computing methodologies ~ Supervised learning byclassification • Applied computing~Health care informationsystems

Item Type: Conference Publication
Authors/Creators:Xia, T and Cosgrove, J and Alty, J and Jamieson, S and Smith, J
Keywords: Parkinson's, movement analysis, artificial intelligence, figure copying task
Publisher: Association for Computing Machinery
DOI / ID Number: 10.1145/3319619.3326822
Copyright Information:

Copyright 2019 Association for Computing Machinery

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