Analysis and Mapping of Machine Learning in the Context of Diabetes
The results of this study are a rich scientific source of ML for diabetes to guide researchers. This study can guide policymakers, physicians, and practitioners to help in the decision-making process. In addition, the findings will be useful for governments to guide future budgets for target studies.
Authors
- Arezoo Ghamgosar (ORCID: https://orcid.org/0000-0001-8805-0325)
- Zahra Galavi (ORCID: https://orcid.org/0000-0003-1179-0055)
- Somaye Norouzi (ORCID: https://orcid.org/0000-0003-1695-3810)
- Leila Nemati‐Anaraki (ORCID: https://orcid.org/0000-0002-9436-2533)
- Leila Ahmadian (ORCID: https://orcid.org/0000-0002-6487-2209)
- Maryam Zarghani (ORCID: https://orcid.org/0000-0002-8702-1298)
- Hooman Bazri
Institutions
- Hamedan University of Medical Sciences (IR)
- Iran University of Medical Sciences (IR)
- Guilan University of Medical Sciences (IR)
- Kerman University of Medical Sciences (IR)
- Zabol University of Medical Sciences (IR)
- Islamic Azad University, Fasa Branch (IR)
- Health Information Management (BE)
- Sabzevar University of Medical Sciences (IR)
Publication Details
- Journal
- Health Science Reports
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/hsr2.71167
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88