Machine learning-assisted prediction of pneumonia based on non-invasive measures.

Journal: Frontiers in public health
Published Date: Jul 28, 2022

Abstract

BACKGROUND: Pneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features.

Authors

  • Clement Yaw Effah
    College of Public Health, Zhengzhou University, Zhengzhou, China.
  • Ruoqi Miao
    College of Public Health, Zhengzhou University, Zhengzhou, China.
  • Emmanuel Kwateng Drokow
    Department of Radiation Oncology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Henan, China.
  • Clement Agboyibor
    School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
  • Ruiping Qiao
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Yongjun Wu
    Department of Health Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, China. wuyongjun@zzu.edu.cn.
  • Lijun Miao
    The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China.
  • Yanbin Wang
    Center of Health Management, General Hospital of Anyang Iron and Steel Group Co., Ltd, Anyang, China.

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