Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia
This study highlights a variety of machine learning (ML) models that can be effectively used to monitor and classify medication adherence in diabetic patients in Ethiopia. However, to fully realize the potential impact of digital health applications, further studies that include patients from diverse settings are necessary. Such research could enhance the generalizability of these models and provide insights into the broader applicability of digital tools for improving medication adherence and patient outcomes in varying healthcare contexts.
Authors
- Biruk Beletew Abate (ORCID: https://orcid.org/0000-0003-0833-2504)
- Bekele Mulat Enyew
- Ashenafi Kibret Sendekie (ORCID: https://orcid.org/0000-0001-5982-853X)
- Ewunate Assaye Kassaw (ORCID: https://orcid.org/0000-0003-2323-2114)
Institutions
- Curtin University (AU)
- Woldia University (ET)
- Indian Institute of Technology Delhi (IN)
- University of Gondar (ET)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-03-20
- DOI
- https://doi.org/10.3389/fendo.2025.1486350
- Citations
- 2
- Primary Topic
- Medication Adherence and Compliance
- Type
- article
- Field-Weighted Citation Impact
- 3.03