- Volume 17 Issue 8
DOI QR Code
Development of T2DM Prediction Model Using RNN
RNN을 이용한 제2형 당뇨병 예측모델 개발
- Jang, Jin-Su (BK21PLUS Program in Embodiment: Health-Society Interaction, Department of Health Science, Graduate School, Korea University) ;
- Lee, Min-Jun (BK21PLUS Program in Embodiment: Health-Society Interaction, Department of Health Science, Graduate School, Korea University) ;
- Lee, Tae-Ro (BK21PLUS Program in Embodiment: Health-Society Interaction, Department of Health Science, Graduate School, Korea University)
- 장진수 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단) ;
- 이민준 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단) ;
- 이태노 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단)
- Received : 2019.05.15
- Accepted : 2019.08.20
- Published : 2019.08.28
Type 2 diabetes mellitus(T2DM) is included in metabolic disorders characterized by hyperglycemia, which causes many complications, and requires long-term treatment resulting in massive medical expenses each year. There have been many studies to solve this problem, but the existing studies have not been accurate by learning and predicting the data at specific time point. Thus, this study proposed a model using RNN to increase the accuracy of prediction of T2DM. This work propose a T2DM prediction model based on Korean Genome and Epidemiology study(Ansan, Anseong Korea). We trained all of the data over time to create prediction model of diabetes. To verify the results of the prediction model, we compared the accuracy with the existing machine learning methods, LR, k-NN, and SVM. Proposed prediction model accuracy was 0.92 and the AUC was 0.92, which were higher than the other. Therefore predicting the onset of T2DM by using the proposed diabetes prediction model in this study, it could lead to healthier lifestyle and hyperglycemic control resulting in lower risk of diabetes by alerted diabetes occurrence.
T2DM;Disease Prediction;Machine Learning;Deep Learning;RNN;Medical AI
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