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Open AccessArticle

A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

1
Cancer Big Data Center, National Cancer Control Institute, National Cancer Center, Goyang 10408, Korea
2
Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea
3
Division of Nephrology, Department of Internal Medicine, Department of Internal Medicine, National Cancer Center, Goyang 10408, Korea
4
Department of Cancer Control and Policy, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang 10408, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 421; https://doi.org/10.3390/app10010421
Received: 4 November 2019 / Revised: 2 January 2020 / Accepted: 3 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Data Technology Applications in Life, Diseases, and Health)
A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care. View Full-Text
Keywords: undiagnosed diabetes mellitus; screening model; non-invasive variables; deep neural network undiagnosed diabetes mellitus; screening model; non-invasive variables; deep neural network
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Ryu, K.S.; Lee, S.W.; Batbaatar, E.; Lee, J.W.; Choi, K.S.; Cha, H.S. A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes. Appl. Sci. 2020, 10, 421.

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