Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm
These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.
Authors
- Azizallah Dehghan (ORCID: https://orcid.org/0000-0002-7345-0796)
- Mehdi Sharafi (ORCID: https://orcid.org/0000-0002-6505-9463)
- Ghasem Nikfar
- Yunes Jahani (ORCID: https://orcid.org/0000-0002-6808-7101)
- Mohsen Khaleghi (ORCID: https://orcid.org/0000-0002-5395-4460)
- Zahra Arefzadeh (ORCID: https://orcid.org/0009-0002-4080-5148)
- Maryam Talebi Moghaddam (ORCID: https://orcid.org/0000-0003-2407-2124)
Institutions
- Kerman University of Medical Sciences (IR)
- Islamic Azad University, Fasa Branch (IR)
- Hormozgan University of Medical Sciences (IR)
- Fasa University of Medical Sciences (IR)
- Persian Gulf University (IR)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1186/s12874-024-02341-z
- Citations
- 19
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 13.45
Funders
- Fasa University of Medical Sciences