Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)

Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)

Abstract 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 epidemiology and identified prominent factors associated with CVD...

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Bibliographic Details
Main Authors: Jia-Xin Li, Li Li, 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
Format: Article
Language:English
Published: BMC 2022-12-01
Series:Global Health Research and Policy
Subjects:
Cardiovascular disease (CVD)
Prediction
Prominent factors
Machine learning
Kashgar prefecture
Online Access:https://doi.org/10.1186/s41256-022-00282-y

Internet

https://doi.org/10.1186/s41256-022-00282-y

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