Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

Zebin, Tahmina, Peek, Niels and Casson, Alexander J (2019) Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks. In: International Conference of the IEEE Engineering in Medicine and Biology Society, 2019-07-23 - 2019-07-27, City Cube Berlin. (In Press)

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Abstract

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: serious mental illnesses,machine learning,uk biobank
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 27 Nov 2019 01:23
Last Modified: 25 Aug 2020 00:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/73013
DOI:

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