Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.

Journal: Computers in biology and medicine
PMID: 39933273

Abstract

INTRODUCTION: Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart disease. There is a gap in the literature in understanding early predictors of diabetes among patients with prediabetes living in underserved communities in the United States. Hence, this study's objective is to identify factors influencing the transition from prediabetes to diabetes in rural or underserved communities using a machine learning approach.

Authors

  • Arinze Nkemdirim Okere
    College of Pharmacy, The University of Iowa, 180 South Grand Ave, 366B College of Pharmacy Building (CPB), Iowa City, IA, 52242, USA. Electronic address: arinzechukwu-okere@uiowa.edu.
  • Tianfeng Li
    Economic, Social, and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Health, Florida A&M University, Tallahassee, FL, 32307, USA. Electronic address: Tianfeng1.li@famu.edu.
  • Carlos Theran
    Department of Computer & Information Sciences, Florida A&M University, Tallahassee, FL, 32307, USA. Electronic address: carlos.theran@famu.edu.
  • Eunice Nyasani
    Walgreens, 1640 South Main Street, Athol, Massachusetts, USA. Electronic address: enyasani@outlook.com.
  • Askal Ayalew Ali
    Economic, Social, and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Health, Florida A&M University, Tallahassee, FL, 32307, USA. Electronic address: askal.ali@famu.edu.

Keywords

External Resources

Popular Topics
Recent Journals