Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.

Journal: International journal of chronic obstructive pulmonary disease
PMID: 39525518

Abstract

BACKGROUND AND PURPOSE: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD.

Authors

  • Zehua Yang
  • Yamei Zheng
    Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, Hainan, 570311, People's Republic of China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Wenya Xu
    Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, Hainan, 570311, People's Republic of China.
  • Haihong Wu
    Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, Hainan, 570311, People's Republic of China.
  • Tian Xie
    Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.
  • Yipeng Ding
    Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, Hainan, 570311, People's Republic of China.

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