Screening for Prediabetes Using Machine Learning Models

• Published in: Computational and mathematical methods in medicine 2014-01, Vol.2014 (2014), p.1-8
• Main Authors: Choi, Soo Beom, Kim, Won Jae, Yoo, Tae Keun, Park, Jee Soo, Chung, Jai Won, Lee, Yong-ho, Kang, Eun Seok, Kim, Deok Won
• Format: Article
• Language: English
• Subjects: Adult; Area Under Curve; Humans; Male; Neural Networks (Computer); Prediabetic State - diagnosis; Random Allocation; Republic of Korea; Risk Factors; ROC Curve; Support Vector Machine
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2014/618976

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n=4685) were used for training and internal validation, while data from KNHANES 2011 (n=4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.