Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort
Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort
Benjamin Lam
1
, BSc ;
Michael Catt
2
, PhD ;
Sophie Cassidy
3
, PhD ;
Jaume Bacardit
1
, PhD ;
Philip Darke
1
, BSc ;
Sam Butterfield
1
, BSc ;
Ossama Alshabrawy
4
, PhD ;
Michael Trenell
5
, PhD ;
Paolo Missier
1
, PhD
1
School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
2
Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
3
Faculty of Medicine and Health, University of Sydney, Sydney, Australia
4
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
5
Faculty of Medical Sciences, The Medical School, Newcastle University, Newcastle upon Tyne, United Kingdom
Corresponding Author:
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Benjamin Lam, BSc
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School of Computing
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Newcastle University
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Urban Sciences Building
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1 Science Square
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Newcastle upon Tyne, NE4 5TG
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United Kingdom
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Phone:
44 7704111910
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Email: b.p.lam1@ncl.ac.uk