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Machine learning identifies prominent factors associated with cardiovascular disease:findings from two million adults in the Kashgar Prospective Cohort Study(KPCS)
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作者 Jia-Xin Li Li Li +13 位作者 Xuemei Zhong Shu-Jun Fan Tao Cen Jianquan Wang chuanjiang he Zhoubin Zhang Ya-Na Luo Xiao-Xuan Liu Li-Xin Hu Yi-Dan Zhang Hui-Ling Qiu Guang-Hui Dong Xiao-Guang Zou Bo-Yi Yang 《Global Health Research and Policy》 2022年第1期10-22,共13页
Background:Identifying factors associated with cardiovascular disease(CVD)is critical for its prevention,but this topic is scarcely investigated in Kashgar prefecture,Xinjiang,northwestern China.We thus explored the C... Background:Identifying factors associated with cardiovascular disease(CVD)is critical for its prevention,but this topic is scarcely investigated in Kashgar prefecture,Xinjiang,northwestern China.We thus explored the CVD epidemi-ology and identified prominent factors associated with CVD in this region.Methods:A total of 1,887,710 adults at baseline(in 2017)of the Kashgar Prospective Cohort Study were included in the analysis.Sixteen candidate factors,including seven demographic factors,4 lifestyle factors,and 5 clinical factors,were collected from a questionnaire and health examination records.CVD was defined according to International Clinical Diagnosis(ICD-10)codes.We first used logistic regression models to investigate the association between each of the candidate factors and CVD.Then,we employed 3 machine learning methods-Random Forest,Random Ferns,and Extreme Gradient Boosting-to rank and identify prominent factors associated with CVD.Stratification analyses by sex,ethnicity,education level,economic status,and residential setting were also performed to test the consistency of the ranking.Results:The prevalence of CVD in Kashgar prefecture was 8.1%.All the 16 candidate factors were confirmed to be significantly associated with CVD(odds ratios ranged from 1.03 to 2.99,all p values<0.05)in logistic regression models.Further machine learning-based analysis suggested that age,occupation,hypertension,exercise frequency,and dietary pattern were the five most prominent factors associated with CVD.The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample.Conclusions:CVD is a major public health concern in Kashgar prefecture.Age,occupation,hypertension,exercise frequency,and dietary pattern might be the prominent factors associated with CVD in this region.In the future,these factors should be given priority in preventing CVD in future. 展开更多
关键词 Cardiovascular disease(CVD) Prediction Prominent factors Machine learning Kashgar prefecture
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