This paper is in the following e-collection/theme issue:

Artificial Intelligence in Diabetes Care and Prevention (11) Wearable Devices and Sensors (676) Fitness Trackers and Smart Pedometers/Accelerometers (501) Digital Biomarkers and Digital Phenotyping (145) Machine Learning (1053)

Published on in Vol 6 , No 1 (2021) :Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23364, first published .
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

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

Authors of this article:

Benjamin Lam 1 Author Orcid Image ;   Michael Catt 2 Author Orcid Image ;   Sophie Cassidy 3 Author Orcid Image ;   Jaume Bacardit 1 Author Orcid Image ;   Philip Darke 1 Author Orcid Image ;   Sam Butterfield 1 Author Orcid Image ;   Ossama Alshabrawy 4 Author Orcid Image ;   Michael Trenell 5 Author Orcid Image ;   Paolo Missier 1 Author Orcid Image
Article Authors Cited by (5) Tweetations (10) Metrics

Journals

  1. Jang B, Kim I. Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data. Biomarkers in Medicine 2021;15(16):1529 View
  2. Fregoso-Aparicio L, Noguez J, Montesinos L, García-García J. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 2021;13(1) View
  3. Clark S, Lomax N, Morris M, Pontin F, Birkin M. Clustering Accelerometer Activity Patterns from the UK Biobank Cohort. Sensors 2021;21(24):8220 View
  4. Mistry S, Riches N, Gouripeddi R, Facelli J. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artificial Intelligence in Medicine 2023;135:102461 View

Books/Policy Documents

  1. Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View