Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort (Preprint)

Benjamin Lam; Michael Catt; Sophie Cassidy; Jaume Bacardit; Philip Darke; Sam Butterfield; Ossama Alshabrawy; Michael Trenell; Paolo Missier

doi:10.2196/preprints.23364

Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort (Preprint)

Benjamin Lam
, Michael Catt
, Sophie Cassidy
, Jaume Bacardit
, Philip Darke
, Sam Butterfield
, Ossama Alshabrawy
, Michael Trenell
, Paolo Missier

Computer Science

Research output: Working paper/Preprint › Preprint

Original language	English
DOIs	https://doi.org/10.2196/preprints.23364
Publication status	Published - 6 Sept 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.2196/preprints.23364

Cite this

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AU - Missier, Paolo

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