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보건의료 빅데이터를 활용한 생활습관 군집현상과 만성질환 유병의 연관성 분석: 지역사회건강조사 원시자료를 활용하여

Association between Clustering of Lifestyle and Chronic Disease Using Healthcare Big Data

박상찬|노을희
한국보건정보통계학회 (The Korean Society of Health informatics statistics)|2020.05|보건정보통계학회지 (Journal of Health Informatics and Statistics)|제45권 제2호|pp. 113-123|DOI https://doi.org/10.21032/jhis.2020.45.2.113

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키워드

Association rule analysisKorean Community Health SurveyChronic diseaseHealthcare big dataClustering of lifestyle

초록

Objectives: In this study, the correlation between the clustering of lifestyle and the prevalence of chronic diseases was identified with the association rule analysis utilizing raw data of the Korean Community Health Survey by Korea Centers for Disease Control and Prevention (KCDC). Methods: It identi￾fied the characteristics of the subject and selected the representative type of cluster through the association rule analysis. In addition, through the chi￾square test, the sensitivity and specificity value, it tested the significance and validity of the association rule. Through the logistic regression analysis, identified the individual characteristic factors having influence on the cluster of lifestyle. Results: The statistically significant association rule was mainly composed of two or three lifestyle clusters and that composed of five lifestyle clusters the most was shown. In addition, there was lifestyle cluster shown commonly in the their age groups in all the chronic diseases and on the contrary, there was the lifestyle cluster shown in specific age group only. Also the specificity of all lifestyle clusters were represented greater than the sensitivity. In the results of identifying the association of individual factors by selecting the lifestyle cluster shown commonly in all chronic diseases in all age groups, the gender, residential area, and occupation showed the signifi￾cant association in all clusters. Conclusions: It presented that drawing the meaningful results by applying the association rule analysis on the health care Big Data is possible. Particularly, the fact that the association rule showing specific pattern between the lifestyle and the chronic disease was discovered provides the important implication in correcting the lifestyle for chronic disease management and prevention.

Abstract

Objectives: In this study, the correlation between the clustering of lifestyle and the prevalence of chronic diseases was identified with the association rule analysis utilizing raw data of the Korean Community Health Survey by Korea Centers for Disease Control and Prevention (KCDC). Methods: It identi￾fied the characteristics of the subject and selected the representative type of cluster through the association rule analysis. In addition, through the chi￾square test, the sensitivity and specificity value, it tested the significance and validity of the association rule. Through the logistic regression analysis, identified the individual characteristic factors having influence on the cluster of lifestyle. Results: The statistically significant association rule was mainly composed of two or three lifestyle clusters and that composed of five lifestyle clusters the most was shown. In addition, there was lifestyle cluster shown commonly in the their age groups in all the chronic diseases and on the contrary, there was the lifestyle cluster shown in specific age group only. Also the specificity of all lifestyle clusters were represented greater than the sensitivity. In the results of identifying the association of individual factors by selecting the lifestyle cluster shown commonly in all chronic diseases in all age groups, the gender, residential area, and occupation showed the signifi￾cant association in all clusters. Conclusions: It presented that drawing the meaningful results by applying the association rule analysis on the health care Big Data is possible. Particularly, the fact that the association rule showing specific pattern between the lifestyle and the chronic disease was discovered provides the important implication in correcting the lifestyle for chronic disease management and prevention.

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