The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus.

Journal: Expert review of endocrinology & metabolism
Published Date: Sep 8, 2024

Abstract

BACKGROUND: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model's outcomes with the validated American Diabetes Association's (ADA) risk assessment regarding predicting people with high risk for T2DM.

Authors

  • Abdullah Alkattan
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Abdullah Al-Zeer
    Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
  • Fahad Alsaawi
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Alanoud Alyahya
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Raghad Alnasser
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Raoom Alsarhan
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Mona Almusawi
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Deemah Alabdulaali
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Nagla Mahmoud
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Rami Al-Jafar
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Faisal Aldayel
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Mustafa Hassanein
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Alhan Haji
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Abdulrahman Alsheikh
    Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
  • Amal Alfaifi
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Elfadil Elkagam
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Ahmed Alfridi
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Amjad Alfaleh
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Khaled Alabdulkareem
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Nashwa Radwan
    Department of Research, Training and Development, Assisting Deputyship for Primary Health Care, Ministry of Health, Riyadh, Saudi Arabia.
  • Edward W Gregg
    School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Ireland.

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