NDLI: Screening for pre-diabetes using support vector machine model

Content Provider	IEEE Xplore Digital Library
Author	Jai Won Chung Won Jae Kim Soo Beom Choi Jee Soo Park Deok Won Kim
Copyright Year	2014
Description	Author affiliation: Dept. of Med. Eng., Yonsei Univ., Seoul, South Korea (Deok Won Kim) \|\| Brain Korea 21 PLUS Project for Med. Sci., Yonsei Univ., Seoul, South Korea (Soo Beom Choi) \|\| Grad. Program in Biomed. Eng., Yonsei Univ., Seoul, South Korea (Jai Won Chung) \|\| Dept. of Med., Yonsei Univ., Seoul, South Korea (Won Jae Kim; Jee Soo Park)
Abstract	The global prevalence of diabetes is rapidly increasing. Studies support screening and interventions for pre-diabetes, which results in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for pre-diabetes that could assist with decreasing the prevalence of diabetes through early identification and subsequent interventions. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4,685) were used for training and internal validation, while data from KNHANES 2011 (n = 4,566) were used for external validation. We developed a model to screen for pre-diabetes using support vector machine (SVM), and performed a systematic evaluation of the SVM model using internal and external validation. We compared the performance of the SVM model with that of a screening score model based on logistic regression analysis for pre-diabetes that had been developed previously. Backward elimination logistic regression resulted in associations between pre-diabetes and age, sex, waist circumference, body mass index, alcohol intake, family history of diabetes, and hypertension. The areas under the curves (AUCs) for the SVM model in the internal and external datasets were 0.761 and 0.731, respectively, while the AUCs for the screening score model were 0.734 and 0.712, respectively. The SVM model developed in this study performed better than the screening score model that had been developed previously and may be more effective for pre-diabetes screening.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2472
Ending Page	2475
File Size	684626
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944123
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Delays Stability criteria Numerical stability Time-varying systems Asymptotic stability Discrete-time systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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