디지털융복합연구 (Journal of Digital Convergence)

  • 제17권8호
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  • Pages.249-255
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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RNN을 이용한 제2형 당뇨병 예측모델 개발

Development of T2DM Prediction Model Using RNN

  • 장진수 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단) ;
  • 이민준 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단) ;
  • 이태노 (고려대학교 대학원 보건과학과 BK21플러스 인간생명-사회환경 상호작용융합사업단)
  • 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)
  • 투고 : 2019.05.15
  • 심사 : 2019.08.20
  • 발행 : 2019.08.28
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초록

제2형 당뇨병은 고혈당이 특징인 대사성 분비 장애로 여러 합병증을 야기하는 질병이며, 장기적인 치료가 필요하기 때문에 매년 많은 의료비를 지출한다. 이를 해결하기 위해 많은 연구들이 있어왔지만, 기존의 연구들은 한 시점에서의 데이터를 학습시켜 예측함으로써 정확도가 높지 않았다. 그래서 본 연구는 제2형 당뇨병 발생 예측에 대한 정확도를 높이기 위하여 RNN을 이용한 모델을 제안하였다. 본 모델을 개발하기 위해 한국인유전체역학조사 지역사회 코호트(안산 안성) 데이터를 이용하였으며, 시간의 흐름에 따른 데이터들을 모두 학습시켜 당뇨병 발생 예측모델을 만들었다. 예측 모델의 성능을 검증하기 위해 기존의 기계 학습 방법인 LR, k-NN, SVM과 정확도를 비교하였다. 비교한 결과 제안한 예측모델의 accuracy는 0.92, AUC는 0.92로 다른 기계 학습 방법보다 높은 정확도를 보였다. 따라서 본 연구에서 제안한 제2형 당뇨병 발생 예측 모델을 활용하여 발병을 조기 예측함으로써 생활습관 개선 및 혈당조절을 통해 당뇨병 발병을 예방하고 늦출 수 있을 것이다.

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.

키워드

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