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A bimodal dataset for diabetes research
Jiandun Li1, Huiyao Zheng1, Yabin Zhou1
1School of Electronic Information Engineering, Shanghai Dianji University, Shanghai, 201306, China.
Scientific Data
|March 12, 2026
Summary
This study introduces a large diabetes dataset with 5,922 examples and 190 attributes. This resource aids research into diabetes progression, complication risks, and related metabolic factors.
Area of Science:
- Endocrinology and Metabolism
- Clinical Data Science
Background:
- Rising global diabetes prevalence necessitates advanced research tools.
- Limited availability of large-scale, comprehensive diabetes datasets hinders progress.
Purpose of the Study:
- To present a novel, large-scale diabetes dataset for research.
- To facilitate in-depth analysis of diabetes progression and complications.
Main Methods:
- Dataset compilation of 5,922 patient examples.
- Inclusion of 190 detailed clinical and demographic attributes.
Main Results:
- The dataset encompasses BMI, lifestyle, family history, glycemic control, insulin, and metabolic profiles.
- Provides a foundation for reliable analyses of diabetes-related factors.
Conclusions:
- This dataset addresses the critical need for comprehensive diabetes data.
- Enables advanced research into diabetes pathogenesis, risk factors, and management.
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