Statistical Analysis for Risk Factors and Prediction of Hypertension based on Health Behavior Information

건강행위정보기반 고혈압 위험인자 및 예측을 위한 통계분석

  • Heo, Byeong Mun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Kim, Sang Yeob (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
  • 허병문 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 김상엽 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 류근호 (충북대학교 데이터베이스/바이오인포매틱스 연구실)
  • Received : 2018.03.30
  • Accepted : 2018.04.27
  • Published : 2018.04.30


The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.

본 연구는 통계분석을 이용한 중년 성인의 고혈압 예측모델 개발이 목적이다. 국민건강영양조사자료(2013년-2016년)를 사용하여 통계분석과 예측모델을 개발하였다. 이진 로지스틱 회귀분석으로 통계적 유의한 고혈압 위험인자를 제시하였으며, Wrapper 변수선택기법을 적용한 로지스틱회귀와 나이브베이즈 알고리즘을 이용하여 예측모델을 개발하였다. 통계분석에서 고혈압에 가장 높은 연관성을 갖는 인자는 남성에서 WHtR (p<0.0001, OR = 2.0242), 여성에서 AGE(p<0.0001, OR = 3.9185)로 나타났다. 예측모델의 성능평가에서, 로지스틱 회귀 모델이 남성(AUC = 0.782)과 여성(AUC = 0.858)에서 가장 좋은 예측력을 보였다. 우리의 연구 결과는 고혈압에 대한 대규모 스크리링 도구를 개발하는데 중요한 정보를 제공하며, 고혈압 연구에 대한 기반정보로 활용할 수 있다.



Supported by : 정보통신기술진흥센터, 한국산업기술진흥원(KIAT)


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