[논문]머신러닝 기반 생애주기별 고혈압 위험 요인 분석

강성안; 김소희; 류민호

doi:10.9723/jksiis.2022.27.5.073

[국내논문] 머신러닝 기반 생애주기별 고혈압 위험 요인 분석
Analysis of Hypertension Risk Factors by Life Cycle Based on Machine Learning 원문보기

한국산업정보학회논문지 = Journal of the Korea Industrial Information Systems Research, v.27 no.5, 2022년, pp.73 - 82

강성안 (동아대학교 경영정보학과) , 김소희 (동아대학교 경영정보학과) , 류민호 (동아대학교 경영정보학과)

초록
AI-Helper

고혈압과 같은 만성질환은 발병의 원인은 다양한 요인들이 복합적으로 작용하기 때문에 생애주기에 따라 차별화된 관리가 필요하다. 본 연구는 머신러닝을 이용해 고혈압 발병에 영향을 미치는 요인들의 생애주기별로 차이를 분석한다. 이를 위해, 질병관리청의 국민건강영양조사 데이터에 대한 전처리 및 변수 선택 과정을 거쳐 총 35개의 변수를 활용했다. 분석결과, 트리기반 머신러닝 모델 중 XGBoost가 중년과 노년 모두 예측 성능이 높은 모델로 나타났다. 변수중요도를 통해 도출된 생애주기별 고혈압 위험요인을 살펴보면 중년의 경우 개인특성 요인, 유전적 요인, 영양섭취 요인이 고혈압 위험요인으로 나타났고, 노년의 경우 영양섭취 요인, 식생활 요인, 생활습관 요인이 고혈압 위험요인으로 도출되었다. 본 연구 결과는 생애주기별 고혈압 관리에 유용한 기초자료로 사용될 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

주제어

표/그림 (6)

표 Table 1 Number of data used for analysis
그림 Fig. 1 Confusion Matrix / Performance Indicators
표 Table 2 Final Variable
표 Table 3 Detect Model Performance Indicators
표 Table 4 The Importance of Variables by Life Cycle
표 Table 5 The Importance of Variables by Life Cycle in Old age

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