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

Machine Learning (1378) Clinical Information and Decision Making (1320) New Methods (906) Public (e)Health, Digital Epidemiology and Public Health Informatics (640) Research Instruments, Questionnaires, and Tools (540) Decision Support for Health Professionals (1132) Tools, Programs and Algorithms (242) Artificial Intelligence in Diabetes Care and Prevention (22) Diabetes Prevention (77)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16850, first published .
Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

Authors of this article:

Lei Zhang1 Author Orcid Image ;   Xianwen Shang2 Author Orcid Image ;   Subhashaan Sreedharan2 Author Orcid Image ;   Xixi Yan2 Author Orcid Image ;   Jianbin Liu2 Author Orcid Image ;   Stuart Keel2 Author Orcid Image ;   Jinrong Wu2 Author Orcid Image ;   Wei Peng3 Author Orcid Image ;   Mingguang He2 Author Orcid Image
Article Authors Cited by (23) Tweetations (2) Metrics

Journals

  1. Guo S, Yu X, Okan O. Moving Health Literacy Research and Practice towards a Vision of Equity, Precision and Transparency. International Journal of Environmental Research and Public Health 2020;17(20):7650 View
  2. Bao Y, Medland N, Fairley C, Wu J, Shang X, Chow E, Xu X, Ge Z, Zhuang X, Zhang L. Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches. Journal of Infection 2021;82(1):48 View
  3. Matricciani L, Paquet C, Dumuid D, Lushington K, Olds T. Multidimensional Sleep and Cardiometabolic Risk Factors for Type 2 Diabetes: Examining Self-Report and Objective Dimensions of Sleep. The Science of Diabetes Self-Management and Care 2022;48(6):533 View
  4. Liu J, Liu M, Chai Z, Li C, Wang Y, Shen M, Zhuang G, Zhang L. Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030. The Lancet Regional Health - Western Pacific 2023;33:100700 View
  5. Kulzer B. Digitale Prävention des Typ-2-Diabetes. Public Health Forum 2021;29(4):297 View
  6. Wu J, Wang Y, Xiao X, Shang X, He M, Zhang L. Spatial Analysis of Incidence of Diagnosed Type 2 Diabetes Mellitus and Its Association With Obesity and Physical Inactivity. Frontiers in Endocrinology 2021;12 View
  7. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
  8. Yang C, Liu Q, Guo H, Zhang M, Zhang L, Zhang G, Zeng J, Huang Z, Meng Q, Cui Y. Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study. Frontiers in Medicine 2021;8 View
  9. Shrestha B, Alsadoon A, Prasad P, Al-Naymat G, Al-Dala’in T, Rashid T, Alsadoon O. Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM. Multimedia Tools and Applications 2022;81(27):38945 View
  10. Kulzer B. Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?. Der Diabetologe 2021;17(8):799 View
  11. Xue L, Wang H, He Y, Sui M, Li H, Mei L, Ying X. Incidence and risk factors of diabetes mellitus in the Chinese population: a dynamic cohort study. BMJ Open 2022;12(11):e060730 View
  12. Rashid M, Askari M, Chen C, Liang Y, Shu K, Cinar A. Artificial Intelligence Algorithms for Treatment of Diabetes. Algorithms 2022;15(9):299 View
  13. Vettoretti M, Di Camillo B. A Variable Ranking Method for Machine Learning Models with Correlated Features: In-Silico Validation and Application for Diabetes Prediction. Applied Sciences 2021;11(16):7740 View
  14. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  15. Sun X, Qiu W, Wu J, Ding S, Wu R. Associations between the levels of circulating inflammatory adipokines and the risk of type 2 diabetes in Chinese male individuals: A case–control study. Journal of Clinical Laboratory Analysis 2023;37(6) View
  16. Simmons S. Strikes and Gutters: Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries. Heliyon 2023;9(9):e19494 View
  17. Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine 2023;4(10):101213 View
  18. Mohsen F, Al-Absi H, Yousri N, El Hajj N, Shah Z. A scoping review of artificial intelligence-based methods for diabetes risk prediction. npj Digital Medicine 2023;6(1) View
  19. Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Scientific Reports 2023;13(1) View
  20. nimmagadda S, Suryanarayana G, Kumar G, Anudeep G, Sai G. A Comprehensive Survey on Diabetes Type-2 (T2D) Forecast Using Machine Learning. Archives of Computational Methods in Engineering 2024 View
  21. Yang H, Chen Z, Huang J, Li S, Alex S. AWD-stacking: An enhanced ensemble learning model for predicting glucose levels. PLOS ONE 2024;19(2):e0291594 View