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

Emmi Antikainen (Corresponding author), Patrick Cella, Antti Tolonen, Mark van Gils

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-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 publication43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages5
Publication statusAccepted/In press - 16 Jul 2021
MoE publication typeA4 Article in a conference publication
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society -
Duration: 30 Oct 20215 Nov 2021
Conference number: 43
http://embc.embs.orf/2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC
Period30/10/215/11/21
Internet address

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