A Comparative Study of Predictive Factors for Hypertension using Logistic Regression Analysis and Decision Tree Analysis

• 영문명: A Comparative Study of Predictive Factors for Hypertension using Logistic Regression Analysis and Decision Tree Analysis
• 발행기관: 물리치료재활과학회
• 저자명: 김소현 조성현
• 간행물 정보: 『Physical Therapy Rehabilitation Science』제12권 제2호, 80~91쪽, 전체 12쪽
• 주제분류: 의약학 > 재활의학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

Objective: The purpose of this study is to identify factors that affect the incidence of hypertension using logistic regression and decision tree analysis, and to build and compare predictive models. Design: Secondary data analysis studyMethods: We analyzed 9,859 subjects from the Korean health panel annual 2019 data provided by the Korea Institute for Health and Social Affairs and National Health Insurance Service. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In logistic regression analysis, those who were 60 years of age or older (Odds ratio, OR＝68.801, p<0.001), those who were divorced/widowhood/separated (OR＝1.377, p<0.001), those who graduated from middle school or younger (OR＝1, reference), those who did not walk at all (OR＝1, reference), those who were obese (OR＝5.109, p<0.001), and those who had poor subjective health status (OR＝2.163, p<0.001) were more likely to develop hypertension. In the decision tree, those over 60 years of age, overweight or obese, and those who graduated from middle school or younger had the highest probability of developing hypertension at 83.3%. Logistic regression analysis showed a specificity of 85.3% and sensitivity of 47.9%; while decision tree analysis showed a specificity of 81.9% and sensitivity of 52.9%. In classification accuracy, logistic regression and decision tree analysis showed 73.6% and 72.6% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. It is thought that both analysis methods can be used as useful data for constructing a predictive model for hypertension.

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키워드

Data mining Decision tree Logistic regression analysis Machine learning Hypertension