Salt intake belief, knowledge, and behavior: a cross-sectional study of older rural Chinese adults.
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ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC4979810 | biostudies-literature | 2016 Aug
REPOSITORIES: biostudies-literature
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Salt intake belief, knowledge, and behavior: a cross-sectional study of older rural Chinese adults.
Zhang Jing J Wu Tao T Chu Hongling H Feng Xiangxian X Shi Jingpu J Zhang Ruijuan R Zhang Yuhong Y Zhang Jianxin J Li Nicole N Yan Lijing L Niu Wenyi W Wu Yangfeng Y
Medicine 20160801 31
Excess sodium consumption is a major cause of high blood pressure and subsequent vascular disease. However, the factors driving people's salt intake behavior remains largely unknown. This study aims to assess the relationship of salt intake behaviors with knowledge and belief on salt and health among older adults in rural China.A cross-sectional survey was conducted among 4693 older participants (men ≥50 and women ≥60 years old) randomly selected from 120 rural villages in 5 northern provinces i ...[more]
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Project description:BackgroundCoronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.MethodsIn this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.ResultsResults demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.ConclusionThe CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.
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