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

• Main Authors: Jia-Xin Li (Author), Li Li (Author), Xuemei Zhong (Author), Shu-Jun Fan (Author), Tao Cen (Author), Jianquan Wang (Author), Chuanjiang He (Author), Zhoubin Zhang (Author), Ya-Na Luo (Author), Xiao-Xuan Liu (Author), Li-Xin Hu (Author), Yi-Dan Zhang (Author), Hui-Ling Qiu (Author), Guang-Hui Dong (Author), Xiao-Guang Zou (Author), Bo-Yi Yang (Author)
• Format: Book
• Published: BMC, 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

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...