Influencing factors of metabolic syndrome among adults in Nanjing， China： an analysis based on decision tree and logistic regress models
10.19428/j.cnki.sjpm.2023.22306
- VernacularTitle:基于logistic回归与决策树模型的南京市居民代谢综合征影响因素分析
- Author:
Yinghao CHEN
1
;
Zhuling YAO
1
;
Zhiyong WANG
1
;
Fei XU
1
Author Information
1. School of Public Health, Nanjing Medical University,Nanjing, Jiangsu 210003,China
- Publication Type:Journal Article
- Keywords:
logistic regression;
decision tree model;
metabolic syndrome;
influencing factor
- From:
Shanghai Journal of Preventive Medicine
2023;35(1):8-14
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveWe analyzed the prevalence of metabolic syndrome in adult residents of Nanjing and explored its influencing factors in order to provide technical references for the prevention of metabolic syndrome. MethodsBased on the data of the Nanjing adult chronic disease thematic survey from January 2017 to June 2018， the influencing factors of metabolic syndrome were analyzed using multifactorial logistic regression model and decision tree model. ResultsThe weighted prevalence of metabolic syndrome among people aged 18 years and over in Nanjing was 16.14%（95%CI：16.12%‒16.16%）. Prevalence of metabolic syndrome was statistically different（P<0.05）among respondents with different demographic characteristics. Logistic regression model analysis showed that age， gender， education， physical activity level， marriage status， smoking status， drinking status， weight status， diabetes and hypertension family history were the influencing factors for the prevalence of metabolic syndrome（P<0.05）. The results of the decision tree model showed that weight status was the most influential factor for metabolic syndrome， followed by age， gender， diabetes family history and smoking status. ConclusionThe prevalence of metabolic syndrome is high among the adult population in Nanjing， and special attention should be paid to middle-aged and elderly men who are overweight and obese， have a family history of diabetes and smoking.
