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

Machine Learning (1465) Clinical Information and Decision Making (1407) New Methods (944) Public (e)Health, Digital Epidemiology and Public Health Informatics (725) Research Instruments, Questionnaires, and Tools (576) Decision Support for Health Professionals (1185) Tools, Programs and Algorithms (266) Artificial Intelligence in Diabetes Care and Prevention (29) Diabetes Prevention (83)

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

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  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;31(5):2905 View
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