Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.

Journal: BMC medical informatics and decision making
PMID: 39901185

Abstract

BACKGROUND: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations are crucial. However, current CVD risk models inadequately consider AO as an independent risk factor. Therefore, developing an accurate risk prediction model can help identify and intervene early.

Authors

  • Xiyu Cao
    Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
  • Jianli Ma
    Harbin Medical University Cancer Hospital, Harbin, China.
  • Xiaoyi He
    Columbia University, New York, NY, USA.
  • Yufei Liu
    China Agricultural University, Beijing 100083, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Chuantao Zhang
    Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China. zhangchuantao@cdutcm.edu.cn.

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