Analyzing classification and feature selection strategies for diabetes prediction across diverse diabetes datasets
Both approaches are found to assign considerable importance to features like age, family history of diabetes, polyuria, polydipsia, and high blood pressure, which are strongly associated with diabetes. In this age of data-driven healthcare, the research presented here aspires to substantially improve healthcare outcomes.
Authors
- Shadi Atalla (ORCID: https://orcid.org/0000-0003-3017-9243)
- R. R. Rithik
- Yassine Himeur (ORCID: https://orcid.org/0000-0001-8904-5587)
- Jayakumar Kaliappan (ORCID: https://orcid.org/0000-0002-6044-6667)
- Kathiravan Srinivasan (ORCID: https://orcid.org/0000-0002-9352-0237)
- Wathiq Mansoor (ORCID: https://orcid.org/0000-0003-2784-5188)
- Yashbir Singh (ORCID: https://orcid.org/0000-0002-5848-7072)
- Diana V. Vera-Garcia (ORCID: https://orcid.org/0000-0002-0520-3582)
- Indrajeet Kumar (ORCID: https://orcid.org/0000-0001-5853-1611)
- S. Sundaravelan
- T. Anesh
Institutions
- University of Dubai (AE)
- Mayo Clinic in Arizona (US)
- Vellore Institute of Technology University (IN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-08-21
- DOI
- https://doi.org/10.3389/frai.2024.1421751
- Citations
- 27
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 19.11