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

Artificial Intelligence in Diabetes Care and Prevention (23) Wearable Devices and Sensors (825) Fitness Trackers and Smart Pedometers/Accelerometers (543) Digital Biomarkers and Digital Phenotyping (178) Machine Learning (1400)

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 Lam1 Author Orcid Image ;   Michael Catt2 Author Orcid Image ;   Sophie Cassidy3 Author Orcid Image ;   Jaume Bacardit1 Author Orcid Image ;   Philip Darke1 Author Orcid Image ;   Sam Butterfield1 Author Orcid Image ;   Ossama Alshabrawy4 Author Orcid Image ;   Michael Trenell5 Author Orcid Image ;   Paolo Missier1 Author Orcid Image
Article Authors Cited by (14) 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
  5. Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix J, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. International Journal of Medical Informatics 2023;179:105209 View
  6. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
  7. Chellappan D, Rajaguru H. Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance. Diagnostics 2023;13(16):2654 View
  8. Site A, Nurmi J, Lohan E. Machine-Learning-Based Diabetes Prediction Using Multisensor Data. IEEE Sensors Journal 2023;23(22):28370 View
  9. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  10. Muse E, Topol E. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metabolism 2024;36(4):670 View
  11. Alsadi B, Musleh S, Al-Absi H, Refaee M, Qureshi R, El Hajj N, Alam T. An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health. BMC Medical Informatics and Decision Making 2024;24(1) View

Books/Policy Documents

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