Predict multicategory causes of death in lung cancer patients using clinicopathologic factors.

Journal: Computers in biology and medicine
Published Date: Feb 1, 2021

Abstract

BACKGROUND: Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other hand, multicategory COD are difficult to classify in lung cancer patients, largely because they have multiple labels (versus binary labels).

Authors

  • Fei Deng
  • Haijun Zhou
    Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, USA.
  • Yong Lin
    Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.
  • John A Heim
    Princeton Medical Center, Plainsboro, NJ, USA.
  • Lanlan Shen
    Department of Pediatrics, Baylor College of Medicine, USDA/ARS Children's Nutrition Research Center, Houston, TX, USA.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Lanjing Zhang
    Rutgers Cancer Institute of New Jersey, Rutgers, New Brunswick, NJ, USA; Princeton Medical Center, Plainsboro, NJ, USA; Department of Biological Sciences, Rutgers University, Newark, NJ, USA; Department of Chemical Biology, Rutgers Ernest Mario School of Pharmacy, Rutgers University, Piscataway, Newark, USA. Electronic address: lanjing.zhang@rutgers.edu.

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