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Accepted for/Published in: JMIRx Med

Date Submitted: May 3, 2024
Date Accepted: May 3, 2024

The final, peer-reviewed published version of this preprint can be found here:

Authors’ Response to Peer Reviews of “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals”

Oyebola K, Ligali F, Owoloye A, Erinwusi B, Alo Y, Musa A, Aina O, Salako B

Authors’ Response to Peer Reviews of “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals”

JMIRx Med 2024;5:e60174

DOI: 10.2196/60174

PMCID: 11441451

Author(s)’ Responses to Peer Review Reports

  • Kolapo Oyebola; 
  • Funmilayo Ligali; 
  • Afolabi Owoloye; 
  • Blessing Erinwusi; 
  • Yetunde Alo; 
  • Adesola Musa; 
  • Oluwagbemiga Aina; 
  • Babatunde Salako

ABSTRACT

his is the author(s)’ responses to peer review reports related to MS ID56993.


 Citation

Please cite as:

Oyebola K, Ligali F, Owoloye A, Erinwusi B, Alo Y, Musa A, Aina O, Salako B

Authors’ Response to Peer Reviews of “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals”

JMIRx Med 2024;5:e60174

DOI: 10.2196/60174

PMCID: 11441451

Per the author's request the PDF is not available.

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