SPECT Image Features for Early Detection of Parkinson's Disease using Machine Learning Methods

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Millions of people around the world suffer from Parkinson's disease, a neurodegenerative disorder with no remedy. Currently, the best response to interventions is achieved when the disease is diagnosed at an early stage. Supervised machine learning models are a common approach to assist early diagnosis from clinical data, but their performance is highly dependent on available example data and selected input features. In this study, we explore 23 single photon emission computed tomography (SPECT) image features for the early diagnosis of Parkinson's disease on 646 subjects. We achieve 94 % balanced classification accuracy in independent test data using the full feature space and show that matching accuracy can be achieved with only eight features, including original features introduced in this study. All the presented features can be generated using a routinely available clinical software and are therefore straightforward to extract and apply.

Original languageEnglish
Title of host publication2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-5 Nov. 2021
PublisherIEEE
Pages2773-2777
Number of pages5
DOIs
Publication statusPublished - 2021
Publication typeA4 Article in a conference publication
EventAnnual International Conference of the IEEE Engineering in Medicine and Biology Society -
Duration: 1 Jan 1900 → …

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Number43
ISSN (Print)2375-7477

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
Period1/01/00 → …

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Medicine(all)

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