A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

Applied Sciences

JaeWook Lee¹  KuiSon Choi¹  HyoSoung Cha¹  KwangSun Ryu¹  SangWon Lee¹  Erdenebileg Batbaatar² 

[1] Cancer Big Data Center, National Cancer Control Institute, National Cancer Center, Goyang 10408, Korea;Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea;

关键词: undiagnosed diabetes mellitus;    screening model;    non-invasive variables;    deep neural network;   

DOI  :  10.3390/app10010421

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown