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Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study

Author

Listed:
  • Kwang Sun Ryu

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    K.S.R. and H.Y.J.K. were equally contributed to this work.)

  • Ha Ye Jin Kang

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    K.S.R. and H.Y.J.K. were equally contributed to this work.)

  • Sang Won Lee

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Hyun Woo Park

    (Healthcare AI Team, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Na Young You

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Jae Ho Kim

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Yul Hwangbo

    (Healthcare AI Team, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    Division of Endocrinology, Department of Internal Medicine, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Kui Son Choi

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

  • Hyo Soung Cha

    (Cancer Big Data Center, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    Healthcare AI Team, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea
    Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Korea)

Abstract

A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010–2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system.

Suggested Citation

  • Kwang Sun Ryu & Ha Ye Jin Kang & Sang Won Lee & Hyun Woo Park & Na Young You & Jae Ho Kim & Yul Hwangbo & Kui Son Choi & Hyo Soung Cha, 2020. "Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8903-:d:453891
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    Citations

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    Cited by:

    1. Miroslava Nedyalkova & Sergio Madurga & Vasil Simeonov, 2021. "Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2," IJERPH, MDPI, vol. 18(4), pages 1-10, February.
    2. Sangwon Lee & Kwang Sun Ryu & Ha Ye Jin Kang & Na Young You & Kui Son Choi & Yul Hwangbo & Jae Wook Lee & Hyo Soung Cha, 2021. "Risk Factors of Undiagnosed Diabetes Mellitus among Korean Adults: A National Cross-Sectional Study Using the KNHANES Data," IJERPH, MDPI, vol. 18(3), pages 1-17, January.

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