Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

Journal: Frontiers in endocrinology
PMID: 40182636

Abstract

BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine learning (ML) algorithms to predict medication adherence within the Ethiopian population. The primary objective of this study was to develop and evaluate ML models designed to classify and monitor medication adherence levels among patients with type 2 diabetes in Ethiopia, to improve patient care and health outcomes.

Authors

  • Ewunate Assaye Kassaw
    Department of Biomedical Engineering, Institute of Technology, University of Gondar, Gondar, Ethiopia.
  • Ashenafi Kibret Sendekie
    Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Bekele Mulat Enyew
    Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia.
  • Biruk Beletew Abate
    College of Health Science, Woldia University, Woldia, Ethiopia.

Keywords

External Resources

Popular Topics
Recent Journals