Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data.

Journal: Frontiers in public health
PMID: 39555038

Abstract

BACKGROUND: Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to develop machine learning models to predict the risk of developing CKD within 1 and 5 years using health examination data.

Authors

  • Yuki Yoshizaki
    Department of Medical Informatics and Statistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
  • Kiminori Kato
    Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
  • Kazuya Fujihara
    Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
  • Hirohito Sone
    Faculty of Medicine, Department of Hematology, Endocrinology and Metabolism, Niigata University, Niigata, Japan.
  • Kohei Akazawa
    Department of Medical Informatics, Niigata University Medical and Dental Hospital, Niigata, Japan.

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